Triple
T8688702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Expo Porte de Versailles |
E206230
|
entity |
| Predicate | hasNumberOfPavilions |
P19114
|
FINISHED |
| Object | 8 |
—
|
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: 8 | Statement: [Paris Expo Porte de Versailles, hasNumberOfPavilions, 8]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfPavilions Context triple: [Paris Expo Porte de Versailles, hasNumberOfPavilions, 8]
-
A.
numberOfPavilions
chosen
Indicates the total count of pavilions associated with a given entity or context.
-
B.
hasNumberOfPalaces
Indicates the specific count of palaces associated with an entity.
-
C.
hasPavilion
Indicates that one entity possesses, includes, or is associated with a pavilion as part of its structure, property, or facilities.
-
D.
hasPavilionFunction
Indicates that something serves the role or function of a pavilion, such as providing a designated space or facility for specific activities or purposes.
-
E.
hasNumberOfMuseums
Indicates the quantity of museums associated with a given entity.
- 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_69ca835481fc819084e33d3bc883bfa6 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc57334b0c8190903a5a1784e74791 |
completed | March 31, 2026, 11:22 p.m. |
| PD | Predicate disambiguation | batch_69cc4569f9048190b9c86b4c81103d35 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:33 p.m.