Triple
T36655240
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Terminal 4 (Ninoy Aquino International Airport) |
E904969
|
entity |
| Predicate | hasFoodOutlets |
P40355
|
FINISHED |
| Object | yes |
—
|
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: yes | Statement: [Terminal 4 (Ninoy Aquino International Airport), hasFoodOutlets, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFoodOutlets Context triple: [Terminal 4 (Ninoy Aquino International Airport), hasFoodOutlets, yes]
-
A.
hasFoodVendors
Indicates that one entity provides or hosts food vendors that operate at or within another entity.
-
B.
hasStreetFood
Indicates that one entity offers, features, or is associated with street food in relation to another entity.
-
C.
hasRestaurantType
Indicates that an entity is associated with or classified as a particular type or category of restaurant.
-
D.
hasRestaurantsAndCafes
chosen
Indicates that the subject location contains or provides access to restaurants and cafés.
-
E.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars 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_69f76e6e3b908190970251b30f76ad71 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fe91383a1c81909266e40c3c3ede6c |
completed | May 9, 2026, 1:43 a.m. |
| PD | Predicate disambiguation | batch_69fe8fde094081908f0f121664fbb5c7 |
completed | May 9, 2026, 1:37 a.m. |
Created at: May 3, 2026, 4:11 p.m.