Triple
T14446729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 100 Famous Japanese Mountains |
E358227
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Mount Kita |
E205585
|
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: Mount Kita | Statement: [100 Famous Japanese Mountains, hasPart, Mount Kita]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mount Kita Context triple: [100 Famous Japanese Mountains, hasPart, Mount Kita]
-
A.
Mount Kita
chosen
Mount Kita is Japan's second-highest mountain, a prominent peak in the Akaishi Mountains renowned for its alpine scenery and popular hiking routes.
-
B.
Mount Kinka
Mount Kinka is a prominent forested mountain in Gifu, Japan, known for its scenic views, hiking trails, and the historic Gifu Castle at its summit.
-
C.
Mount Tsurumi
Mount Tsurumi is a volcanic mountain in Ōita Prefecture, Japan, known for its panoramic views, seasonal foliage, and ropeway access from the hot spring resort city of Beppu.
-
D.
Mount Kinugasa
Mount Kinugasa is a Japanese mountain whose name was notably given to the Imperial Japanese Navy cruiser Kinugasa.
-
E.
Mount Ohiri
Mount Ohiri is the highest peak on the French Polynesian island of Taha'a in the Society Islands archipelago.
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de9160126c8190a2862a1a3dde1aff |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe9db810c481908dde925ff90f3fa0 |
completed | May 9, 2026, 2:36 a.m. |
Created at: April 10, 2026, 1:19 a.m.