Triple
T24813175
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | East Street Market |
E620843
|
entity |
| Predicate | numberOfStallsApproximate |
P43082
|
FINISHED |
| Object | over 250 |
—
|
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 250 | Statement: [East Street Market, numberOfStallsApproximate, over 250]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfStallsApproximate Context triple: [East Street Market, numberOfStallsApproximate, over 250]
-
A.
numberOfStalls
chosen
Indicates the quantity of stalls associated with or contained within a given entity or location.
-
B.
hasTypeOfStalls
Indicates that an entity features or includes stalls of a particular type or category.
-
C.
numberOfParkingSpaces
Indicates the total count of parking spaces associated with a particular entity or location.
-
D.
numberOfBays
Indicates the count of distinct bays associated with or contained within a given entity.
-
E.
numberOfStandingPlaces
Indicates the total count of standing-only positions or spots available in a given context (e.g., a vehicle, venue, or area).
- 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_69e2fabfd4648190bd0e5c7f4dbb6cab |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f63fd6c68481908c542aa03e297b9c |
completed | May 2, 2026, 6:17 p.m. |
| PD | Predicate disambiguation | batch_69f63c6456608190b94e7c2e2c2a4824 |
completed | May 2, 2026, 6:03 p.m. |
Created at: April 18, 2026, 4:55 a.m.