Triple
T13186085
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hager |
E313855
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Kristin Hager
Kristin Hager is a Canadian actress best known for her roles in film and television, including the series "Being Human."
|
E1074468
|
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: Kristin Hager | Statement: [Hager, hasNotableBearer, Kristin Hager]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kristin Hager Context triple: [Hager, hasNotableBearer, Kristin Hager]
-
A.
Kristin Hudson
Kristin Hudson is the wife of R&B singer Montel Jordan and has been involved with him in faith-based relationship and marriage ministry work.
-
B.
Kristin Burr
Kristin Burr is a film producer known for her work on major studio projects, including Disney live-action adaptations such as "Cruella."
-
C.
Kristina Hetherington
Kristina Hetherington is a film editor known for her work on the psychological period drama "The Wonder."
-
D.
Kristen Ruhlin
Kristen Ruhlin is an American actress and writer best known for her work in independent films and comedy projects.
-
E.
Kristin Westphalen
Kristin Westphalen is a central character in the science fiction TV series "seaQuest DSV," serving as the submarine's chief medical officer and science advisor.
- 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: Kristin Hager Triple: [Hager, hasNotableBearer, Kristin Hager]
Generated description
Kristin Hager is a Canadian actress best known for her roles in film and television, including the series "Being Human."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kristin Hager Target entity description: Kristin Hager is a Canadian actress best known for her roles in film and television, including the series "Being Human."
-
A.
Kristin Hudson
Kristin Hudson is the wife of R&B singer Montel Jordan and has been involved with him in faith-based relationship and marriage ministry work.
-
B.
Kristin Burr
Kristin Burr is a film producer known for her work on major studio projects, including Disney live-action adaptations such as "Cruella."
-
C.
Kristina Hetherington
Kristina Hetherington is a film editor known for her work on the psychological period drama "The Wonder."
-
D.
Kristen Ruhlin
Kristen Ruhlin is an American actress and writer best known for her work in independent films and comedy projects.
-
E.
Kristin Westphalen
Kristin Westphalen is a central character in the science fiction TV series "seaQuest DSV," serving as the submarine's chief medical officer and science advisor.
- 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_69d806ae1e08819090d95bfe1538cc17 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d98c4b663c8190b0b18f0785f7b57d |
completed | April 10, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbac754aec8190a5b975c9965eef61 |
completed | May 6, 2026, 9:02 p.m. |
| NEDg | Description generation | batch_69fbb16aa3dc819096407243383d7578 |
completed | May 6, 2026, 9:23 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fbb1ebe7048190b62c56b43002f94c |
completed | May 6, 2026, 9:26 p.m. |
Created at: April 9, 2026, 9:15 p.m.