Triple
T26835628
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | B&B Hotels |
E675621
|
entity |
| Predicate | hasHotelNear |
P61766
|
FINISHED |
| Object | Disneyland Paris |
—
|
NE NERFINISHED |
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: Disneyland Paris | Statement: [B&B Hotels, hasHotelNear, Disneyland Paris]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHotelNear Context triple: [B&B Hotels, hasHotelNear, Disneyland Paris]
-
A.
hasNearbyHotel
chosen
Indicates that one entity is located close to or within a short distance of a hotel.
-
B.
hasAirportHotelNearby
Indicates that an airport has at least one hotel located in its immediate vicinity or within a short travel distance.
-
C.
hasNearbyHotelCluster
Indicates that one or more hotels are located in close proximity to the referenced place or area, forming a spatial cluster.
-
D.
hasNearbyLodge
Indicates that one entity is located close to or in the vicinity of a lodge associated with another entity.
-
E.
hasAttractionNearby
Indicates that one entity is located close to another entity that serves as an attraction or point of interest.
- 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_69eee9b776448190993a60b67fcc9545 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f6d6a6b04c8190bee4cf9c00665ef7 |
completed | May 3, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
Created at: April 27, 2026, 5:04 a.m.