Triple
T28550714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Josee Chouinard |
E722877
|
entity |
| Predicate | hasProfessionalCareer |
P198381
|
FINISHED |
| Object | true |
—
|
LITERAL FINISHED |
How this triple was built (2 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: true | Statement: [Josee Chouinard, hasProfessionalCareer, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProfessionalCareer Context triple: [Josee Chouinard, hasProfessionalCareer, true]
-
A.
hasPlayedProfessionalSports
Indicates that an entity has participated as an athlete in an officially recognized professional-level sports competition or league.
-
B.
hasGivenProfession
Indicates that an entity holds or practices a specified profession or occupation.
-
C.
hasCareerGamesPlayed
Indicates the total number of games an entity has played over the course of its entire career.
-
D.
hasProfessionalStatus
Indicates that an entity holds a particular professional standing, rank, or qualification within a field or occupation.
-
E.
hasCoachedProfessionalSports
Indicates that a person has served in a coaching role for a professional-level sports team or athlete.
- F. None of above. chosen
Provenance (4 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_69f01a60204481909af1bb76247b8221 |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69fee0b2da3c8190a3519d0564f2f32d |
completed | May 9, 2026, 7:22 a.m. |
| PD | Predicate disambiguation | batch_69fee05b315c819081dfcbfb15273487 |
completed | May 9, 2026, 7:20 a.m. |
| PDg | Predicate description generation | batch_69fee0b03a0c81909117dc5915d77295 |
completed | May 9, 2026, 7:22 a.m. |
Created at: April 28, 2026, 3:42 a.m.