Triple
T35752287
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Baku City Circuit |
E1033348
|
entity |
| Predicate | turn1Type |
P138015
|
FINISHED |
| Object | left-hand corner |
—
|
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: left-hand corner | Statement: [Baku City Circuit, turn1Type, left-hand corner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: turn1Type Context triple: [Baku City Circuit, turn1Type, left-hand corner]
-
A.
turnType
chosen
Indicates the specific kind or category of turn being made in a movement or path (e.g., left turn, right turn, U-turn).
-
B.
turnoutType
Indicates the specific kind or category of turnout (e.g., how or in what form participation or attendance occurs) associated with an event or activity.
-
C.
turns
Indicates a change in orientation, direction, or state initiated by one entity affecting itself or another entity.
-
D.
turnsIn
Indicates that an entity submits or hands over something, typically work or an item, to another party or authority.
-
E.
turnoverType
Indicates the specific category or nature of a turnover event, such as how or why control of an asset, position, or role changes from one party to another.
- 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_69f76e1262f48190a313318665acc189 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a197aee48190bbd69f670a3f7721 |
completed | May 3, 2026, 7:27 p.m. |
| PD | Predicate disambiguation | batch_69f7a070e23881909a233370acb57384 |
completed | May 3, 2026, 7:22 p.m. |
Created at: May 3, 2026, 4:06 p.m.