Triple
T8688704
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paris Expo Porte de Versailles |
E206230
|
entity |
| Predicate | hasLargestHallArea |
P32773
|
FINISHED |
| Object | about 51000 square metres |
—
|
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: about 51000 square metres | Statement: [Paris Expo Porte de Versailles, hasLargestHallArea, about 51000 square metres]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLargestHallArea Context triple: [Paris Expo Porte de Versailles, hasLargestHallArea, about 51000 square metres]
-
A.
hasMainHall
Indicates that an entity possesses or includes a primary or central hall as a significant internal space.
-
B.
largestVenueOf
Indicates that one venue is the largest (typically by capacity, area, or scale) among a specified set or within a particular context.
-
C.
hasLargestStudioAreaSquareMetres
Indicates that the subject entity possesses the studio with the greatest area, measured in square metres, compared to relevant alternatives.
-
D.
hasMainHallType
Indicates the specific category or kind of main hall associated with an entity.
-
E.
hasLargestAreaOf
chosen
Indicates that the subject entity possesses the greatest area (size of surface or region) compared to the other entities in the specified set or context.
- 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.