Triple
T268437
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apple Pie Bakery Café |
E5783
|
entity |
| Predicate | hasServiceModel |
P849
|
FINISHED |
| Object | counter service |
—
|
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: counter service | Statement: [Apple Pie Bakery Café, hasServiceModel, counter service]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasServiceModel Context triple: [Apple Pie Bakery Café, hasServiceModel, counter service]
-
A.
hasServiceTo
Indicates that one entity provides, offers, or operates a service for or directed toward another entity.
-
B.
hasServiceType
chosen
Indicates that an entity is associated with or categorized by a particular type of service.
-
C.
hasServiceClass
Indicates that an entity is associated with, or categorized under, a particular class or type of service.
-
D.
hasFormerService
Indicates that an entity previously provided a service to another entity but no longer does so.
-
E.
hasModelType
Indicates that an entity is associated with or classified under a specific model type.
- 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_69a2587daeb081909591b9d30f80a271 |
completed | Feb. 28, 2026, 2:52 a.m. |
| NER | Named-entity recognition | batch_69a25dae4a0c8190a66cf6ed3889851c |
completed | Feb. 28, 2026, 3:14 a.m. |
| PD | Predicate disambiguation | batch_69a25b70d99c819085d8381a313a2a34 |
completed | Feb. 28, 2026, 3:05 a.m. |
Created at: Feb. 28, 2026, 2:56 a.m.