Triple
T7965383
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jurys Inn |
E185188
|
entity |
| Predicate | typicalAmenities |
P79291
|
FINISHED |
| Object | restaurant |
—
|
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: restaurant | Statement: [Jurys Inn, typicalAmenities, restaurant]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: typicalAmenities Context triple: [Jurys Inn, typicalAmenities, restaurant]
-
A.
amenitiesInclude
chosen
Indicates that a place or facility provides or contains specific amenities as part of its features.
-
B.
typicalUnitConfiguration
Indicates the standard or commonly used arrangement, composition, or setup of a unit in a given context.
-
C.
typicalFeatures
Indicates that the related entities are characteristic or commonly occurring features or attributes of something.
-
D.
amenityLevel
Indicates the degree or quality of facilities, services, or conveniences provided in relation to something.
-
E.
typicalUnitType
Indicates that one entity is the standard or commonly used unit type associated with measuring or expressing the other entity.
- 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_69ca8297699481909b75a405f01e03af |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3ba0da588190853dda68bba0755a |
completed | March 31, 2026, 3:12 a.m. |
| PD | Predicate disambiguation | batch_69cb0473d7dc8190a25d0cf460b9fcbe |
completed | March 30, 2026, 11:17 p.m. |
Created at: March 30, 2026, 5:12 p.m.