Triple
T23425723
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Arby’s |
E560789
|
entity |
| Predicate | numberOfLocationsApproximate |
P14032
|
FINISHED |
| Object | over 3000 |
—
|
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 3000 | Statement: [Arby’s, numberOfLocationsApproximate, over 3000]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLocationsApproximate Context triple: [Arby’s, numberOfLocationsApproximate, over 3000]
-
A.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
-
B.
hasNumberOfLocalities
Indicates the relationship that specifies how many localities (e.g., towns, districts, or similar administrative units) are associated with a given entity.
-
C.
hasApproximateNumberOfPeople
Indicates that an entity is associated with an estimated or approximate count of people, rather than an exact number.
-
D.
sectorCountApprox
Indicates that the number of sectors involved is an approximate or estimated count rather than an exact value.
-
E.
numberOfSites
chosen
Indicates the total count of distinct sites associated with or involved in the given entity or context.
- 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_69e2454cb1108190ab21ada5411a7146 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1a54951688190a3c5382971af3e41 |
completed | April 29, 2026, 6:29 a.m. |
| PD | Predicate disambiguation | batch_69f061f92da081908e7f1d0cd1e9b01c |
completed | April 28, 2026, 7:30 a.m. |
Created at: April 17, 2026, 5:47 p.m.