Triple
T8354606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Ontake |
E196652
|
entity |
| Predicate | rankingByHeightInJapan |
P82249
|
FINISHED |
| Object | second highest volcano in Japan |
—
|
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: second highest volcano in Japan | Statement: [Mount Ontake, rankingByHeightInJapan, second highest volcano in Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByHeightInJapan Context triple: [Mount Ontake, rankingByHeightInJapan, second highest volcano in Japan]
-
A.
rankingByHeightInIsrael
Indicates an ordering of entities based on their relative heights within the context of Israel.
-
B.
rankByHeightWorld
Indicates an ordering of entities based on their relative height compared to all others in the world.
-
C.
gdpRankInJapan
Indicates the position of an entity in the ordered ranking of GDP values within Japan.
-
D.
rankAmongTallestBuildings
Indicates that one building is among the tallest buildings within a specified group, area, or category.
-
E.
rankInCityByHeight
Indicates the relative ordering of entities within a specific city based on their height, such as which is tallest, second tallest, and so on.
- F. None of above. chosen
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_69ca82f08b348190bfb7881944bbff6f |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb8048edb88190a1980ad74818b898 |
completed | March 31, 2026, 8:05 a.m. |
| PD | Predicate disambiguation | batch_69cb70ca25548190b0f90c5384e3fb3c |
completed | March 31, 2026, 6:59 a.m. |
| PDg | Predicate description generation | batch_69cb76d823b08190a54fadb50660cda5 |
completed | March 31, 2026, 7:25 a.m. |
Created at: March 30, 2026, 5:59 p.m.