Triple
T10861227
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ian McLellan Hunter |
E256407
|
entity |
| Predicate | child |
P120
|
FINISHED |
| Object |
Jonathan Hunter
Jonathan Hunter is the son of British screenwriter Ian McLellan Hunter, who was known for his work in mid-20th-century cinema.
|
E895964
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Jonathan Hunter | Statement: [Ian McLellan Hunter, child, Jonathan Hunter]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jonathan Hunter Context triple: [Ian McLellan Hunter, child, Jonathan Hunter]
-
A.
Jonathan Hunt
Jonathan Hunt was a prominent early 19th-century American politician and lawyer from Vermont who served multiple terms in the U.S. House of Representatives.
-
B.
Martin Hunter
Martin Hunter is a film editor best known for his work on the science-fiction horror movie "Event Horizon."
-
C.
Danny Hunter
Danny Hunter is a fictional British intelligence officer and key member of MI5’s Section D in the television drama series "Spooks."
-
D.
Ross Hunter
Ross Hunter was a prominent American film producer best known for his lavish, emotionally charged Hollywood melodramas of the 1950s and 1960s.
-
E.
Jonathan J. Hunt
Jonathan J. Hunt is a researcher in machine learning and control who is credited with introducing the Deep Deterministic Policy Gradient (DDPG) algorithm for continuous action reinforcement learning.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Jonathan Hunter Triple: [Ian McLellan Hunter, child, Jonathan Hunter]
Generated description
Jonathan Hunter is the son of British screenwriter Ian McLellan Hunter, who was known for his work in mid-20th-century cinema.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jonathan Hunter Target entity description: Jonathan Hunter is the son of British screenwriter Ian McLellan Hunter, who was known for his work in mid-20th-century cinema.
-
A.
Jonathan Hunt
Jonathan Hunt was a prominent early 19th-century American politician and lawyer from Vermont who served multiple terms in the U.S. House of Representatives.
-
B.
Martin Hunter
Martin Hunter is a film editor best known for his work on the science-fiction horror movie "Event Horizon."
-
C.
Danny Hunter
Danny Hunter is a fictional British intelligence officer and key member of MI5’s Section D in the television drama series "Spooks."
-
D.
Ross Hunter
Ross Hunter was a prominent American film producer best known for his lavish, emotionally charged Hollywood melodramas of the 1950s and 1960s.
-
E.
Jonathan J. Hunt
Jonathan J. Hunt is a researcher in machine learning and control who is credited with introducing the Deep Deterministic Policy Gradient (DDPG) algorithm for continuous action reinforcement learning.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6aa83d1448190a66d93c32394d21f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7515186f08190a5cc388a7d936c4f |
completed | April 9, 2026, 7:12 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e23b8b3fd48190b36e34dc19fa5193 |
completed | April 17, 2026, 1:54 p.m. |
| NEDg | Description generation | batch_69e2453f6f008190847298f4006290f7 |
completed | April 17, 2026, 2:35 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69e288b1d64c8190b31313634b706d0a |
completed | April 17, 2026, 7:23 p.m. |
Created at: April 8, 2026, 9:20 p.m.