Triple
T3111678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lal Bahadur Shastri International Airport |
E64964
|
entity |
| Predicate | hasFoodAndBeverageOutlets |
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: [Lal Bahadur Shastri International Airport, hasFoodAndBeverageOutlets, yes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFoodAndBeverageOutlets Context triple: [Lal Bahadur Shastri International Airport, hasFoodAndBeverageOutlets, yes]
-
A.
hasRestaurantsAndCafes
chosen
Indicates that the subject location contains or provides access to restaurants and cafés.
-
B.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
C.
isDiningDestination
Indicates that a place serves as a destination where people go specifically to eat meals or dine.
-
D.
hasCharacterDining
Indicates that an entity offers or includes dining experiences where guests can eat while interacting with costumed characters.
-
E.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
- 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_69ad857eeaf48190b34ebfdaa7a264cf |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada43b0b3c8190a828c9cfcf730ed9 |
completed | March 8, 2026, 4:30 p.m. |
| PD | Predicate disambiguation | batch_69ad9df25d4c81908ff0f6cff55d0563 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:04 p.m.