Triple
T14088449
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maihama district |
E339058
|
entity |
| Predicate | hasHotelArea |
P25904
|
FINISHED |
| Object | Tokyo Disney Resort official hotels area |
—
|
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: Tokyo Disney Resort official hotels area | Statement: [Maihama district, hasHotelArea, Tokyo Disney Resort official hotels area]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasHotelArea Context triple: [Maihama district, hasHotelArea, Tokyo Disney Resort official hotels area]
-
A.
hasAreaType
Indicates that an entity is associated with a specific kind or classification of area (e.g., urban, rural, coastal).
-
B.
containsResortArea
chosen
Indicates that one location or region includes within its boundaries a designated resort area.
-
C.
hasLandmarkArea
Indicates that a specified area is designated as the landmark area associated with a particular entity or location.
-
D.
hasReservationArea
Indicates that an entity is assigned or associated with a specific reserved area or section designated for its use.
-
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_69d81c687b0c819087fd9ed4198403f8 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de5ee1ce88819091c983286289337e |
completed | April 14, 2026, 3:36 p.m. |
| PD | Predicate disambiguation | batch_69de05b0e6c88190a819eeba0028981f |
completed | April 14, 2026, 9:15 a.m. |
Created at: April 9, 2026, 10:21 p.m.