Triple
T33629242
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | K2K experiment |
E861502
|
entity |
| Predicate | countryOfNearDetector |
P118930
|
FINISHED |
| Object | Japan |
—
|
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: Japan | Statement: [K2K experiment, countryOfNearDetector, Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: countryOfNearDetector Context triple: [K2K experiment, countryOfNearDetector, Japan]
-
A.
nearDetectorCountry
Indicates that the subject entity is geographically close to the country where the detector is located.
-
B.
locatedNearCountry
Indicates that one entity is geographically situated close to the borders or territory of a specified country.
-
C.
farDetectorCountry
Indicates that one entity is the country in which the far (more distant) detector associated with another entity is located.
-
D.
countryClosestTo
Indicates the relationship where one country is geographically nearer to a given reference point or entity than any other country.
-
E.
countryOf
chosen
Indicates that one entity is the country to which another entity belongs, is located in, or is associated with.
- 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_69f34981c54c81909b33c3fa2208a52d |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_6a00c3362dcc819096e49b882709fddd |
completed | May 10, 2026, 5:41 p.m. |
| PD | Predicate disambiguation | batch_6a00c2e5cdd88190a5f2d0f6a843cd56 |
completed | May 10, 2026, 5:39 p.m. |
Created at: May 1, 2026, 1:41 a.m.