Triple
T21051615
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DiGard Racing |
E518598
|
entity |
| Predicate | fieldedCarNumber |
P106324
|
FINISHED |
| Object | 88 |
—
|
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: 88 | Statement: [DiGard Racing, fieldedCarNumber, 88]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: fieldedCarNumber Context triple: [DiGard Racing, fieldedCarNumber, 88]
-
A.
leadingCarNumber
Indicates the identifier or number assigned to the car that is currently in the leading position relative to others.
-
B.
registrationNumber
Indicates the unique identifier assigned to an entity as part of an official or formal registration process.
-
C.
notableVehicleNumber
Indicates that a specific vehicle is identified as notable or significant by a particular number or identifier.
-
D.
carNumberUsed
chosen
Indicates that a specific car number has been used or assigned in a given context or event.
-
E.
fieldedCarBrand
Indicates that a particular car brand has been entered, deployed, or represented (e.g., in a race, event, or lineup) by some agent or organization.
- 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_69e0b5053ac48190921529544959e906 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fd7bd96c81909cb46419ad4e1222 |
completed | April 21, 2026, 4:30 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf6728881908a2a43a5c8804a2a |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:35 p.m.