Triple
T4430177
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bear Bryant |
E95306
|
entity |
| Predicate | totalCareerTies |
P16053
|
FINISHED |
| Object | 17 |
—
|
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: 17 | Statement: [Bear Bryant, totalCareerTies, 17]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: totalCareerTies Context triple: [Bear Bryant, totalCareerTies, 17]
-
A.
hasTieGame
Indicates that a game or match has ended with both sides having the same score, resulting in no winner.
-
B.
gamesTiedAsHeadCoach
Indicates that the subject and object have the same number of games that they have coached which ended in a tie, in their roles as head coaches.
-
C.
tieGamesCount
chosen
Indicates the number of games in a set or series that ended in a tie, with no winner or loser.
-
D.
hasHistoricalTieTo
Indicates a relationship where one entity is historically connected or linked to another through past events, associations, or influences.
-
E.
careerWins
Indicates the total number of wins an individual or entity has accumulated over the course of their entire career.
- F. None of above.
Provenance (3 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_69b3453c2a0c8190926b574c90766db9 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b35569b3388190bdef2568f5dc04ce |
completed | March 13, 2026, 12:08 a.m. |
| PD | Predicate disambiguation | batch_69b34f5eabe88190a12b244ea71e46d6 |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:30 p.m.