Triple
T33368675
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tower of Terror (Tokyo DisneySea) |
E854424
|
entity |
| Predicate | inUniverseHotelName |
P120286
|
FINISHED |
| Object | Hotel Hightower |
—
|
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: Hotel Hightower | Statement: [Tower of Terror (Tokyo DisneySea), inUniverseHotelName, Hotel Hightower]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inUniverseHotelName Context triple: [Tower of Terror (Tokyo DisneySea), inUniverseHotelName, Hotel Hightower]
-
A.
hotelName
chosen
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.
reservationName
Indicates the name or title assigned to a specific reservation in the relationship.
-
D.
hotelOperator
Indicates that an entity operates, manages, or runs a hotel as its responsible service provider or business owner.
-
E.
hasHotelType
Indicates that a hotel is classified as belonging to a specific type or category (e.g., resort, boutique, hostel).
- 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_69f3496bda8c8190bfc8fade9d1b791c |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f79f48acec8190a9d5964581a94f6c |
completed | May 3, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_69f79e4888248190be2f63cdfb5cd7b7 |
completed | May 3, 2026, 7:13 p.m. |
Created at: May 1, 2026, 1:35 a.m.