Triple
T6987493
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Philippine International Convention Center |
E161999
|
entity |
| Predicate | numberOfFunctionRooms |
P2490
|
FINISHED |
| Object | more than 70 |
—
|
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: more than 70 | Statement: [Philippine International Convention Center, numberOfFunctionRooms, more than 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfFunctionRooms Context triple: [Philippine International Convention Center, numberOfFunctionRooms, more than 70]
-
A.
numberOfHotelRooms
Indicates the total count of rooms that a given hotel has.
-
B.
numberOfHalls
chosen
Indicates the quantity of halls associated with a given entity or location.
-
C.
approximateNumberOfRooms
Indicates an estimated or not precisely known count of rooms associated with an entity.
-
D.
numberOfChambers
Indicates the count of distinct chambers or compartments associated with an entity.
-
E.
numberOfBedrooms
Indicates the quantity of bedrooms associated with a given property or dwelling.
- 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_69c68856d7808190ab33ee914640281b |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6dbbd926c8190a8b60527bd553fa3 |
completed | March 27, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c4a18881908d267137daed828b |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:32 p.m.