Triple
T1141851
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pizza Hut |
E23468
|
entity |
| Predicate | hasDriveThroughLocations |
P24379
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Pizza Hut, hasDriveThroughLocations, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDriveThroughLocations Context triple: [Pizza Hut, hasDriveThroughLocations, true]
-
A.
hasScenicDrive
Indicates that one entity offers or features a visually appealing or picturesque driving route associated with it.
-
B.
drivesOn
Indicates that an entity uses or travels along a particular route, surface, or roadway as its path of movement.
-
C.
hasRentalCarCenter
Indicates that a location or facility includes or is associated with a rental car center where vehicles can be rented.
-
D.
hasTransportRoute
Indicates that there exists a designated transportation connection or route linking one entity to another.
-
E.
hasTruckTraffic
Indicates that there is truck-related vehicular movement or flow occurring on or through a specified location or route.
- 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_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. |
| PDg | Predicate description generation | batch_69a4bbb9fb4c81909dd39c496893c21b |
completed | March 1, 2026, 10:20 p.m. |
Created at: March 1, 2026, 7:44 p.m.