Triple
T35359311
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hall of Fame Racing |
E1021427
|
entity |
| Predicate | carNumberUsedInSeason |
P106324
|
FINISHED |
| Object | No. 96 in 2006 |
—
|
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: No. 96 in 2006 | Statement: [Hall of Fame Racing, carNumberUsedInSeason, No. 96 in 2006]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: carNumberUsedInSeason Context triple: [Hall of Fame Racing, carNumberUsedInSeason, No. 96 in 2006]
-
A.
carNumberUsed
chosen
Indicates that a specific car number has been used or assigned in a given context or event.
-
B.
carNumberInFilm
Indicates the specific identifying number assigned to a car as it appears within a particular film.
-
C.
championshipCarNumber
Indicates the specific car number associated with a championship-winning entry in a racing competition.
-
D.
raceNumberInSeason
Indicates the ordinal position of a specific race within the sequence of races in a given season.
-
E.
serialNumberInSeason
Indicates the position or sequence number that something holds within a particular season.
- 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_69f76def44c881908a20e8008572eb44 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79da9f80c8190b0afd8509f28747b |
completed | May 3, 2026, 7:10 p.m. |
| PD | Predicate disambiguation | batch_69f79617d40481909ba372f94209c08b |
completed | May 3, 2026, 6:38 p.m. |
Created at: May 3, 2026, 4:03 p.m.