Triple
T14963501
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kenora Thistles |
E373126
|
entity |
| Predicate | notablePlayer |
P304
|
FINISHED |
| Object |
Tom Hooper
Tom Hooper was a prominent early 20th-century Canadian ice hockey player best known for his role with the Kenora Thistles during their Stanley Cup–winning era.
|
E1130566
|
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: Tom Hooper | Statement: [Kenora Thistles, notablePlayer, Tom Hooper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tom Hooper Context triple: [Kenora Thistles, notablePlayer, Tom Hooper]
-
A.
Tom Hooper
Tom Hooper is an Academy Award–winning British film and television director best known for works such as "The King’s Speech" and "Les Misérables."
-
B.
Joe Wright
Joe Wright is a British film director best known for acclaimed period dramas such as "Pride & Prejudice" and "Atonement."
-
C.
Sam Mendes
Sam Mendes is an acclaimed British film and theatre director known for works such as "American Beauty," the James Bond films "Skyfall" and "Spectre," and the World War I epic "1917."
-
D.
Stephen Daldry
Stephen Daldry is an acclaimed British theatre and film director and producer known for works such as "Billy Elliot," "The Hours," and "The Reader."
-
E.
Simon Gavron
Simon Gavron is the father of British-American actor Rafi Gavron.
- 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: Tom Hooper Triple: [Kenora Thistles, notablePlayer, Tom Hooper]
Generated description
Tom Hooper was a prominent early 20th-century Canadian ice hockey player best known for his role with the Kenora Thistles during their Stanley Cup–winning era.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tom Hooper Target entity description: Tom Hooper was a prominent early 20th-century Canadian ice hockey player best known for his role with the Kenora Thistles during their Stanley Cup–winning era.
-
A.
Tom Hooper
Tom Hooper is an Academy Award–winning British film and television director best known for works such as "The King’s Speech" and "Les Misérables."
-
B.
Joe Wright
Joe Wright is a British film director best known for acclaimed period dramas such as "Pride & Prejudice" and "Atonement."
-
C.
Sam Mendes
Sam Mendes is an acclaimed British film and theatre director known for works such as "American Beauty," the James Bond films "Skyfall" and "Spectre," and the World War I epic "1917."
-
D.
Stephen Daldry
Stephen Daldry is an acclaimed British theatre and film director and producer known for works such as "Billy Elliot," "The Hours," and "The Reader."
-
E.
Simon Gavron
Simon Gavron is the father of British-American actor Rafi Gavron.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6d0487c8190b7754af8c5014b37 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe8bdeec408190893d1db9254da24e |
completed | May 9, 2026, 1:20 a.m. |
| NEDg | Description generation | batch_69fe90383bd081908bc754655c203695 |
completed | May 9, 2026, 1:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fe90ec50e081909ab267fee2b069bc |
completed | May 9, 2026, 1:42 a.m. |
Created at: April 10, 2026, 2:40 a.m.