Triple
T22048967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Pogba |
E544833
|
entity |
| Predicate | youthClub |
P1088
|
FINISHED |
| Object |
US Torcy
US Torcy is a French youth football club known for developing talented players, including future international stars.
|
E1515627
|
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: US Torcy | Statement: [Paul Pogba, youthClub, US Torcy]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: US Torcy Context triple: [Paul Pogba, youthClub, US Torcy]
-
A.
Torry
Torry is a surname most notably associated with American comedian and actor Joe Torry.
-
B.
Torrence
Torrence is a Scottish surname most notably associated with silent film and early Hollywood character actor Ernest Torrence.
-
C.
Tucker
Tucker is the criminal defendant whose custodial interrogation and subsequent appeal led to the U.S. Supreme Court decision in Michigan v. Tucker, which clarified the scope of Miranda-related protections.
-
D.
Tucker
Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
-
E.
Tucker
Tucker is a paranormal investigator character from the Insidious horror film series, known for his tech-based ghost-hunting work alongside his partner Specs.
- 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: US Torcy Triple: [Paul Pogba, youthClub, US Torcy]
Generated description
US Torcy is a French youth football club known for developing talented players, including future international stars.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: US Torcy Target entity description: US Torcy is a French youth football club known for developing talented players, including future international stars.
-
A.
Torry
Torry is a surname most notably associated with American comedian and actor Joe Torry.
-
B.
Torrence
Torrence is a Scottish surname most notably associated with silent film and early Hollywood character actor Ernest Torrence.
-
C.
Tucker
Tucker is the criminal defendant whose custodial interrogation and subsequent appeal led to the U.S. Supreme Court decision in Michigan v. Tucker, which clarified the scope of Miranda-related protections.
-
D.
Tucker
Tucker is a surname most notably associated with Albert W. Tucker, a Canadian-American mathematician and game theorist known for his contributions to topology and the formalization of the prisoner's dilemma.
-
E.
Tucker
Tucker is a paranormal investigator character from the Insidious horror film series, known for his tech-based ghost-hunting work alongside his partner Specs.
- 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_69e11e32445c8190ab97089b48a130bb |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1283191e48190b4ddc84138243327 |
completed | April 28, 2026, 9:35 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0a7b79594c8190b66439167d7a3cbd |
completed | May 18, 2026, 2:37 a.m. |
| NEDg | Description generation | batch_6a0a7cb2b0c88190930290efe1d8371a |
completed | May 18, 2026, 2:42 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0a7e131ae48190a564a86d3d691e1a |
completed | May 18, 2026, 2:48 a.m. |
Created at: April 16, 2026, 8:26 p.m.