Triple
T8739997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Procol Harum |
E207475
|
entity |
| Predicate | hasMember |
P10
|
FINISHED |
| Object |
Ian Hauge
Ian Hauge is a musician known for being a member of the British rock band Procol Harum.
|
E758046
|
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: Ian Hauge | Statement: [Procol Harum, hasMember, Ian Hauge]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ian Hauge Context triple: [Procol Harum, hasMember, Ian Hauge]
-
A.
Douglas Hegdahl
Douglas Hegdahl is a former U.S. Navy sailor and Vietnam War prisoner of war known for his remarkable memory, which he used to secretly record details about fellow POWs while held at the "Hanoi Hilton" and later provide crucial testimony about their treatment.
-
B.
John Rickard
John Rickard is a film producer known for working on mainstream Hollywood comedies and studio features.
-
C.
Warren Skaaren
Warren Skaaren was an American screenwriter and script doctor best known for his work on major 1980s films such as "Beetlejuice" and "Batman."
-
D.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
E.
Tim Lovestedt
Tim Lovestedt is a screenwriter known for his work on the military drama film "Megan Leavey."
- 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: Ian Hauge Triple: [Procol Harum, hasMember, Ian Hauge]
Generated description
Ian Hauge is a musician known for being a member of the British rock band Procol Harum.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Ian Hauge Target entity description: Ian Hauge is a musician known for being a member of the British rock band Procol Harum.
-
A.
Douglas Hegdahl
Douglas Hegdahl is a former U.S. Navy sailor and Vietnam War prisoner of war known for his remarkable memory, which he used to secretly record details about fellow POWs while held at the "Hanoi Hilton" and later provide crucial testimony about their treatment.
-
B.
John Rickard
John Rickard is a film producer known for working on mainstream Hollywood comedies and studio features.
-
C.
Warren Skaaren
Warren Skaaren was an American screenwriter and script doctor best known for his work on major 1980s films such as "Beetlejuice" and "Batman."
-
D.
Kevin Hageman
Kevin Hageman is an American screenwriter and producer known for his work on animated and family films and television series, including contributions to The Lego Movie franchise.
-
E.
Tim Lovestedt
Tim Lovestedt is a screenwriter known for his work on the military drama film "Megan Leavey."
- 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_69ca835a03a081909d4d4cd01a18c9fb |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5d486e34819094a6c6ec26c047cf |
completed | March 31, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cf6ef8d7f88190aea21c82da47e4a0 |
completed | April 3, 2026, 7:40 a.m. |
| NEDg | Description generation | batch_69cf70c981808190856827fbcd4c4671 |
completed | April 3, 2026, 7:48 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69cf71914ec48190bd623d8d773e7ca7 |
completed | April 3, 2026, 7:51 a.m. |
Created at: March 30, 2026, 6:38 p.m.