Triple
T36014674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ian Baker-Finch |
E1041804
|
entity |
| Predicate | otherProfessionalWins |
P184332
|
FINISHED |
| Object | 6 |
—
|
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: 6 | Statement: [Ian Baker-Finch, otherProfessionalWins, 6]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: otherProfessionalWins Context triple: [Ian Baker-Finch, otherProfessionalWins, 6]
-
A.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
-
B.
hasWonProfessionalTournament
Indicates that an entity has achieved victory in at least one professional-level tournament or competition.
-
C.
winnerProfession
Indicates that the associated profession is the occupation or field of work of the winner in a given event or competition.
-
D.
careerManagerialWins
Indicates the total number of games or contests an individual has won in a managerial role over the course of their entire career.
-
E.
wonAgainst
Indicates that one entity achieved victory over another in a competition, conflict, or contest.
- 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_69f76e2b981881908e4e160607fa82eb |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7ad15f3b88190b7c9742a734fec5f |
completed | May 3, 2026, 8:16 p.m. |
| PD | Predicate disambiguation | batch_69f7ab75387c819091afc3c2128eb903 |
completed | May 3, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69f7acad20388190b9b10270ca9bdfbc |
completed | May 3, 2026, 8:14 p.m. |
Created at: May 3, 2026, 4:07 p.m.