Triple
T37647433
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Urayasu |
E937077
|
entity |
| Predicate | hasResortComplex |
P60302
|
FINISHED |
| Object | Tokyo Disney Resort |
E34340
|
NE 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 | Statement: [Urayasu, hasResortComplex, Tokyo Disney Resort]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasResortComplex Context triple: [Urayasu, hasResortComplex, Tokyo Disney Resort]
-
A.
hasResortHotel
Indicates that one entity owns, includes, or is associated with a resort hotel as part of its facilities or offerings.
-
B.
hasResortCenter
Indicates that an entity includes or is associated with a resort facility serving as a central location for leisure or vacation activities.
-
C.
hasResortCommunity
Indicates that one entity includes, contains, or is associated with a resort-style residential or vacation community.
-
D.
isResortOf
Indicates that a location or facility functions as a resort associated with, belonging to, or serving a particular entity (such as a city, region, or organization).
-
E.
partOfResortComplexWith
chosen
Indicates that one entity is a component or facility within the same resort complex as the other entity, forming part of a shared resort property.
- F. None of above.
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_69f76ed4fe908190b8061c5c135e0971 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c8efcd4819088c2aeead65d93df |
completed | May 12, 2026, 7:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a40fb68665c819086d0959577c0e47f |
completed | June 28, 2026, 10:46 a.m. |
| PD | Predicate disambiguation | batch_6a037a1553e08190bb7424c448cb1f33 |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 3, 2026, 4:18 p.m.