Triple
T20998759
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Mesa, New Mexico |
E517223
|
entity |
| Predicate | Chope’s Bar and Café_isKnownFor |
P81236
|
FINISHED |
| Object | New Mexican cuisine |
—
|
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: New Mexican cuisine | Statement: [La Mesa, New Mexico, Chope’s Bar and Café_isKnownFor, New Mexican cuisine]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: Chope’s Bar and Café_isKnownFor Context triple: [La Mesa, New Mexico, Chope’s Bar and Café_isKnownFor, New Mexican cuisine]
-
A.
hasChampagneBar
Indicates that an entity includes, features, or is equipped with a champagne bar.
-
B.
hasRestaurantsAndBars
Indicates that the subject location contains or provides access to both restaurants and bars.
-
C.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
D.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
E.
hasRestaurantType
chosen
Indicates that an entity is associated with or classified as a particular type or category of restaurant.
- 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_69e0b5006e2881909fc2383f841740cc |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6fc2395108190871e173354e4ef6f |
completed | April 21, 2026, 4:25 a.m. |
| PD | Predicate disambiguation | batch_69e5dbec80708190a49bccab7ff97e7b |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 1:51 p.m.