Triple
T31438242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Polanco, Mexico City |
E801993
|
entity |
| Predicate | hasRestaurantScene |
P81236
|
FINISHED |
| Object | fine dining |
—
|
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: fine dining | Statement: [Polanco, Mexico City, hasRestaurantScene, fine dining]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRestaurantScene Context triple: [Polanco, Mexico City, hasRestaurantScene, fine dining]
-
A.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
-
B.
hasCharacterDining
Indicates that an entity offers or includes dining experiences where guests can eat while interacting with costumed characters.
-
C.
hasRestaurantType
chosen
Indicates that an entity is associated with or classified as a particular type or category of restaurant.
-
D.
hasRestaurantsAndBars
Indicates that the subject location contains or provides access to both restaurants and bars.
-
E.
hasRestaurantArea
Indicates that a place or establishment includes a designated area used as a restaurant or for dining services.
- 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_69f348c475348190bf579ca858eec77c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a015ff02814819094806517fc4c69fa |
completed | May 11, 2026, 4:49 a.m. |
| PD | Predicate disambiguation | batch_6a0154ddd3c48190b85f9f48731cfd8f |
completed | May 11, 2026, 4:02 a.m. |
Created at: April 30, 2026, 9:03 p.m.