Triple
T3000883
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mount Kenya |
E81182
|
entity |
| Predicate | highestPeak |
P1674
|
FINISHED |
| Object | Batian |
E318860
|
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: Batian | Statement: [Mount Kenya, highestPeak, Batian]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Batian Context triple: [Mount Kenya, highestPeak, Batian]
-
A.
Batian
chosen
Batian is the highest peak of Mount Kenya, a prominent volcanic mountain in central Kenya.
-
B.
Batabanó
Batabanó is a coastal municipality in western Cuba known for its fishing industry and ferry connections to nearby islands.
-
C.
Butuan
Butuan is a historically significant city in the Caraga region of northeastern Mindanao in the Philippines, known for its rich pre-colonial heritage and archaeological sites.
-
D.
Tabogon
Tabogon is a coastal municipality in the province of Cebu in the Philippines, known for its agricultural lands and scenic seaside areas.
-
E.
Kapyong
Kapyong is a Korean War battlefield in South Korea renowned for a pivotal 1951 engagement in which outnumbered UN forces, including Canadian troops, halted a major Chinese offensive.
- 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_69ad8b187fc8819085914d3c9ea3142d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad9a1022e48190afee77db94635ff2 |
completed | March 8, 2026, 3:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b1dea057a48190a7911d8d6046dd3d |
completed | March 11, 2026, 9:29 p.m. |
Created at: March 8, 2026, 2:59 p.m.