Triple
T6964253
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Istiklal Avenue |
E161446
|
entity |
| Predicate | typicalEstablishment |
P25526
|
FINISHED |
| Object | boutiques |
—
|
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: boutiques | Statement: [Istiklal Avenue, typicalEstablishment, boutiques]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalEstablishment Context triple: [Istiklal Avenue, typicalEstablishment, boutiques]
-
A.
typicalVenues
chosen
Indicates that the specified locations are common or standard places where the associated activity, event, or entity usually occurs or is hosted.
-
B.
alsoEats
Indicates that an entity consumes something in addition to another item or items it already eats.
-
C.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
D.
hasRestaurantsAndCafes
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_69c68853cff881908439d488924a8283 |
completed | March 27, 2026, 1:38 p.m. |
| NER | Named-entity recognition | batch_69c6daf2b7bc8190a3e73f3b24f0352b |
completed | March 27, 2026, 7:30 p.m. |
| PD | Predicate disambiguation | batch_69c6d7c0b0a08190b262dfc94992994d |
completed | March 27, 2026, 7:17 p.m. |
Created at: March 27, 2026, 2:30 p.m.