Triple
T1888446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | West Side Market |
E41813
|
entity |
| Predicate | hasCulinaryFocus |
P17971
|
FINISHED |
| Object | local Cleveland specialties |
—
|
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: local Cleveland specialties | Statement: [West Side Market, hasCulinaryFocus, local Cleveland specialties]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCulinaryFocus Context triple: [West Side Market, hasCulinaryFocus, local Cleveland specialties]
-
A.
hasSpecialtyFood
chosen
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
B.
cuisineFeature
Indicates a characteristic, quality, or notable aspect that describes or distinguishes a particular cuisine.
-
C.
hasCuisineItem
Indicates that a particular cuisine includes, features, or is associated with a specific food item.
-
D.
culinaryUse
Indicates that one entity is used in the preparation, flavoring, or serving of food or drink for another entity.
-
E.
cuisine
Indicates the type or style of food traditionally associated with or served by an entity (such as a restaurant or region).
- 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_69a8864b6de0819098d089f6a1b910a7 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb12382b481908cd26b56f8558226 |
completed | March 7, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69abafe61bc48190ac9ead027df930e1 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.