Triple
T9266773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Westfield Stratford City |
E222720
|
entity |
| Predicate | numberOfRestaurantsAndCafes |
P87876
|
FINISHED |
| Object | over 70 |
—
|
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: over 70 | Statement: [Westfield Stratford City, numberOfRestaurantsAndCafes, over 70]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRestaurantsAndCafes Context triple: [Westfield Stratford City, numberOfRestaurantsAndCafes, over 70]
-
A.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
B.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
-
C.
numberOfRestaurants
Indicates the quantitative count of restaurants associated with a given entity or context.
-
D.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
E.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
- F. None of above. chosen
Provenance (4 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_69ca841f2e808190a64f4c31903a1332 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd074bac9481909419988a9e8d9bd5 |
completed | April 1, 2026, 11:53 a.m. |
| PD | Predicate disambiguation | batch_69cc7a537bbc8190baee71f556e52a7b |
completed | April 1, 2026, 1:52 a.m. |
| PDg | Predicate description generation | batch_69cc95597be081908ece2491dd2f0f74 |
completed | April 1, 2026, 3:47 a.m. |
Created at: March 30, 2026, 7:33 p.m.