Triple
T19230599
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mile End |
E480860
|
entity |
| Predicate | hasNotableFoodCulture |
P89569
|
FINISHED |
| Object | bagel shops |
—
|
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: bagel shops | Statement: [Mile End, hasNotableFoodCulture, bagel shops]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableFoodCulture Context triple: [Mile End, hasNotableFoodCulture, bagel shops]
-
A.
haveDistinctCulinaryTraditions
Indicates that the related entities possess different and distinguishable culinary practices, cuisines, or food-related customs from one another.
-
B.
foodCulture
chosen
Indicates the relationship between a place or group and its characteristic traditions, practices, and preferences surrounding food and eating.
-
C.
haveCuisine
Indicates that an entity (such as a restaurant or place) offers, serves, or is associated with a particular type or style of cuisine.
-
D.
hasSpecialtyFood
Indicates that an entity offers, serves, or is associated with a particular type of specialty food.
-
E.
hasCulturalFeature
Indicates that an entity possesses, includes, or is characterized by a particular cultural element, attribute, or landmark.
- 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_69d8e8ccb8f48190ad420098e74fb1db |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5fa9ce5e081909df994841ce476d5 |
completed | April 20, 2026, 10:06 a.m. |
| PD | Predicate disambiguation | batch_69e4dcfae6f081909cc173cf71a5005c |
completed | April 19, 2026, 1:47 p.m. |
Created at: April 10, 2026, 1:25 p.m.