Triple
T1141831
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pizza Hut |
E23468
|
entity |
| Predicate | numberOfLocations |
P8902
|
FINISHED |
| Object | thousands of restaurants worldwide |
—
|
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: thousands of restaurants worldwide | Statement: [Pizza Hut, numberOfLocations, thousands of restaurants worldwide]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfLocations Context triple: [Pizza Hut, numberOfLocations, thousands of restaurants worldwide]
-
A.
numberOfVenues
Indicates the total count of venues associated with a given entity or context.
-
B.
numberOfSites
Indicates the total count of distinct sites associated with or involved in the given entity or context.
-
C.
numberOfStores
chosen
Indicates the total count of stores associated with a given entity or context.
-
D.
numberOfPositions
Indicates the total count of distinct positions or roles associated with 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_69a493ef399c8190b04b9146d2314f59 |
completed | March 1, 2026, 7:30 p.m. |
| NER | Named-entity recognition | batch_69a4bc4d414881908fc636e8ccbc4c34 |
completed | March 1, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69a4bb4d4104819084027a043c6118cb |
completed | March 1, 2026, 10:18 p.m. |
Created at: March 1, 2026, 7:44 p.m.