Triple
T35564304
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Team Penske No. 22 Ford Mustang (NASCAR Cup Series) |
E1027725
|
entity |
| Predicate | garageNumber |
P183262
|
FINISHED |
| Object | Team Penske Cup program |
—
|
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: Team Penske Cup program | Statement: [Team Penske No. 22 Ford Mustang (NASCAR Cup Series), garageNumber, Team Penske Cup program]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: garageNumber Context triple: [Team Penske No. 22 Ford Mustang (NASCAR Cup Series), garageNumber, Team Penske Cup program]
-
A.
garageLocation
Indicates the physical location where a vehicle or object is stored or garaged.
-
B.
numberOfGarages
Indicates the quantity of garages associated with an entity (such as a property or building).
-
C.
hasGarage
Indicates that one entity possesses or includes a garage associated with it.
-
D.
yardNumber
Indicates the identifying number assigned to a specific yard or yard-related unit within a larger system or location.
-
E.
hasGateNumber
Indicates that an entity (such as a flight or departure) is associated with a specific gate number.
- 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_69f76e020fd8819081cb080e7e203083 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f79a54aa3c8190b2bb5d790b2d42d4 |
completed | May 3, 2026, 6:56 p.m. |
| PD | Predicate disambiguation | batch_69f7961970408190b669cc556e30a608 |
completed | May 3, 2026, 6:38 p.m. |
| PDg | Predicate description generation | batch_69f79a53ccc481908421ae16e69aa8a4 |
completed | May 3, 2026, 6:56 p.m. |
Created at: May 3, 2026, 4:04 p.m.