Triple
T11935367
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lexicon Bracknell |
E284025
|
entity |
| Predicate | hasNumberOfRestaurantsAndCafes |
P87876
|
FINISHED |
| Object | over 20 |
—
|
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 20 | Statement: [The Lexicon Bracknell, hasNumberOfRestaurantsAndCafes, over 20]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNumberOfRestaurantsAndCafes Context triple: [The Lexicon Bracknell, hasNumberOfRestaurantsAndCafes, over 20]
-
A.
hasRestaurantsAndCafes
Indicates that the subject location contains or provides access to restaurants and cafés.
-
B.
numberOfRestaurantsAndCafes
chosen
Indicates the total count of restaurants and cafes associated with a given entity or area.
-
C.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
-
D.
hasRestaurantsAndBars
Indicates that the subject location contains or provides access to both restaurants and bars.
-
E.
hasCafes
Indicates that one entity possesses, contains, or includes one or more cafes within it.
- 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_69d6ab2ce9c48190b5d39511b524f666 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d90306fcf48190a963d2d1932288d1 |
completed | April 10, 2026, 2:02 p.m. |
| PD | Predicate disambiguation | batch_69d8bb3af0188190bfb22be5c97b3349 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:45 p.m.