Triple
T4542351
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gunwharf Quays |
E107563
|
entity |
| Predicate | hasNumberOfRestaurantsAndBars |
P57594
|
FINISHED |
| Object | over 30 |
—
|
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 30 | Statement: [Gunwharf Quays, hasNumberOfRestaurantsAndBars, over 30]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfRestaurantsAndBars Context triple: [Gunwharf Quays, hasNumberOfRestaurantsAndBars, over 30]
-
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.
isDiningDestination
Indicates that a place serves as a destination where people go specifically to eat meals or dine.
-
E.
hasRestaurant
Indicates that one entity possesses, operates, or contains a restaurant associated with it.
- 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_69bd43f922788190b7edfa294e39b178 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd57d3be988190bf118c4a87415613 |
completed | March 20, 2026, 2:21 p.m. |
| PD | Predicate disambiguation | batch_69bd5220e40481908ca2d7e2c43d8531 |
completed | March 20, 2026, 1:56 p.m. |
| PDg | Predicate description generation | batch_69bd56f6e75481909c487a94a2c2d0ba |
completed | March 20, 2026, 2:17 p.m. |
Created at: March 20, 2026, 1:04 p.m.