Triple
T12924423
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Skylon |
E309207
|
entity |
| Predicate | numberOfRevolvingRestaurants |
P107035
|
FINISHED |
| Object | 1 |
—
|
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: 1 | Statement: [Skylon, numberOfRevolvingRestaurants, 1]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfRevolvingRestaurants Context triple: [Skylon, numberOfRevolvingRestaurants, 1]
-
A.
numberOfRestaurants
Indicates the quantitative count of restaurants associated with a given entity or context.
-
B.
numberOfRestaurantsAndRetail
Indicates the total count of entities that are either restaurants or retail establishments associated with a given subject.
-
C.
numberOfRestaurantsAndCafes
Indicates the total count of restaurants and cafes associated with a given entity or area.
-
D.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
-
E.
hasNumberOfRestaurantsAndBars
Indicates the total count of restaurants and bars associated with a given entity.
- 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_69d7bdfa933c8190b5a27aa4a08a19b7 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d971e9576c81908eb59569af6da877 |
completed | April 10, 2026, 9:55 p.m. |
| PD | Predicate disambiguation | batch_69d96fab4d0881909a7a4d66bab9aa85 |
completed | April 10, 2026, 9:46 p.m. |
| PDg | Predicate description generation | batch_69d970f6f5748190ad35aff801db53d5 |
completed | April 10, 2026, 9:51 p.m. |
Created at: April 9, 2026, 5:42 p.m.