Triple
T8662687
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kita |
E205585
|
entity |
| Predicate | rankingByElevationInJapan |
P82249
|
FINISHED |
| Object | second-highest mountain 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 mountain in Japan | Statement: [Mount Kita, rankingByElevationInJapan, second-highest mountain in Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankingByElevationInJapan Context triple: [Mount Kita, rankingByElevationInJapan, second-highest mountain in Japan]
-
A.
rankingByHeightInJapan
chosen
Indicates the relative order of entities based on their height specifically within the context of Japan.
-
B.
prominenceRelativeToKibo
Indicates how prominent or noticeable one entity is in comparison to Kibo.
-
C.
summitElevationRank
Indicates the relative position of a summit in an ordered list based on its elevation compared to other summits.
-
D.
countryHighestPointRank
Indicates the relative ranking of a country's highest natural elevation compared to the highest points of other countries.
-
E.
rankByHeightWorld
Indicates an ordering of entities based on their relative height compared to all others in the world.
- 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_69ca83516ae88190aefe034b3bc589e3 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc4872a190819087d679f3006bd030 |
completed | March 31, 2026, 10:19 p.m. |
| PD | Predicate disambiguation | batch_69cc4564e018819081036722f3e42a71 |
completed | March 31, 2026, 10:06 p.m. |
Created at: March 30, 2026, 6:30 p.m.