Triple
T32424772
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Japanese Grand Prix |
E828547
|
entity |
| Predicate | lapsAtSuzuka |
P174064
|
FINISHED |
| Object | 53 |
—
|
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: 53 | Statement: [Japanese Grand Prix, lapsAtSuzuka, 53]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: lapsAtSuzuka Context triple: [Japanese Grand Prix, lapsAtSuzuka, 53]
-
A.
lapsAtMonza
Indicates that an entity completes one or more laps at the Monza racing circuit.
-
B.
lapsAtAlbertPark
Indicates that an entity completes or records laps at the Albert Park circuit.
-
C.
hasPitLane
Indicates that a racing circuit, track, or similar facility includes a designated pit lane area for vehicle servicing and related activities.
-
D.
achievedPolePositionAt
Indicates that an entity secured the top starting position (pole position) at a specified event or location.
-
E.
hasChicane
Indicates that one entity incorporates or features a chicane (a sharp, S-shaped bend or series of bends), typically in the context of a track, route, or path.
- 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_69f3491b28bc8190b75cea7a507f337b |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c286ac288190843dac21651babd0 |
completed | May 3, 2026, 3:35 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6eb32c8190bf405b2011fa48f7 |
completed | May 3, 2026, 3:01 a.m. |
| PDg | Predicate description generation | batch_69f6bb344bb48190a8089f29c0063ded |
completed | May 3, 2026, 3:04 a.m. |
Created at: May 1, 2026, 12:54 a.m.