Triple
T32575969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hilton Tokyo Bay |
E832645
|
entity |
| Predicate | officialHotelOf |
P199291
|
FINISHED |
| Object | Tokyo Disney Resort |
—
|
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: Tokyo Disney Resort | Statement: [Hilton Tokyo Bay, officialHotelOf, Tokyo Disney Resort]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: officialHotelOf Context triple: [Hilton Tokyo Bay, officialHotelOf, Tokyo Disney Resort]
-
A.
hotelName
Indicates the specific name assigned to a hotel in the relationship.
-
B.
hotelBrand
Indicates that a hotel is affiliated with, operated by, or marketed under a specific hotel brand.
-
C.
hadStationHotel
Indicates that a railway station possessed or was associated with a hotel facility serving its passengers or operations.
-
D.
notableHotel
Indicates that a hotel is notable or significant in some recognized way, such as historical, cultural, or commercial importance.
-
E.
hotelOperator
Indicates that an entity operates, manages, or runs a hotel as its responsible service provider or business owner.
- 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_69f349289adc81909f4374a58ec35a39 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69ff2d22ffb48190ae58ddf3c7e02869 |
completed | May 9, 2026, 12:48 p.m. |
| PD | Predicate disambiguation | batch_69ff2ac2e1c4819096cc64e94aef2ff0 |
completed | May 9, 2026, 12:38 p.m. |
| PDg | Predicate description generation | batch_69ff2d21f0a48190ad2ef5901cb07d93 |
completed | May 9, 2026, 12:48 p.m. |
Created at: May 1, 2026, 1:04 a.m.