Triple
T7318151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Katsuren Castle |
E168466
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Uruma |
E208160
|
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: Uruma | Statement: [Katsuren Castle, locatedIn, Uruma]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Uruma Context triple: [Katsuren Castle, locatedIn, Uruma]
-
A.
Uruma
chosen
Uruma is a coastal city in central Okinawa, Japan, known for its scenic islands, historic sites, and U.S. military bases.
-
B.
Nago
Nago is a coastal city in northern Okinawa, Japan, known for its beaches, subtropical climate, and role as a regional commercial and cultural center.
-
C.
Yokadouma
Yokadouma is a town in eastern Cameroon that serves as an important local administrative and commercial center near the country's forested border regions.
-
D.
Takashima
Takashima is a lakeside city in western Shiga Prefecture, Japan, known for its scenic location along Lake Biwa and surrounding mountains.
-
E.
Takashima
Takashima is a prominent commercial and waterfront district in Nishi-ku, Yokohama, known for major shopping complexes and modern urban development.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69c68a5251508190ad68df4151cfeb04 |
completed | March 27, 2026, 1:46 p.m. |
| NER | Named-entity recognition | batch_69c6ef178b3081908cd0c62466069741 |
completed | March 27, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7eef847948190a0f2066008f63efd |
completed | March 28, 2026, 3:08 p.m. |
Created at: March 27, 2026, 3:02 p.m.