Triple
T4115726
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stade des Costières |
E90285
|
entity |
| Predicate | hasStandCount |
P14729
|
FINISHED |
| Object | 4 |
—
|
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: 4 | Statement: [Stade des Costières, hasStandCount, 4]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasStandCount Context triple: [Stade des Costières, hasStandCount, 4]
-
A.
hasStand
Indicates that an entity possesses, is equipped with, or is supported by a stand or base structure.
-
B.
hasStandsType
Indicates that an entity has or is associated with a particular type or category of stands (e.g., display stands, support stands, or similar structures).
-
C.
hasStandardToeCount
Indicates that an entity possesses the typical or expected number of toes for its kind.
-
D.
hasHeadCount
Indicates that an entity is associated with a specific number of individuals, typically representing the size or count of people (or similar units) related to it.
-
E.
benchCount
chosen
Indicates the number of benches associated with a given entity or location.
- 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_69aed95c080881908125e30c5dcdc6f8 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af03d7240c8190a64dcbc669772808 |
completed | March 9, 2026, 5:31 p.m. |
| PD | Predicate disambiguation | batch_69af0183eb84819087d7184de28f5514 |
completed | March 9, 2026, 5:21 p.m. |
Created at: March 9, 2026, 3:41 p.m.