Triple

T1128826
Position Surface form Disambiguated ID Type / Status
Subject Cameroon E24780 entity
Predicate mountain P10602 FINISHED
Object Mount Cameroon E129891 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 Cameroon | Statement: [Cameroon, mountain, Mount Cameroon]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mount Cameroon
Context triple: [Cameroon, mountain, Mount Cameroon]
  • A. Mount Cameroon chosen
    Mount Cameroon is an active volcanic mountain in southwestern Cameroon and one of the highest peaks in West Africa, rising sharply from the Atlantic coast.
  • B. Monte Cinto
    Monte Cinto is the tallest mountain in Corsica, renowned for its rugged alpine terrain and panoramic views over the island and the Mediterranean Sea.
  • C. Mount Nyangani
    Mount Nyangani is a prominent mountain in eastern Zimbabwe known for its scenic highland landscapes and status as a popular hiking destination.
  • D. Mount Kilimanjaro
    Mount Kilimanjaro is a massive dormant stratovolcano in northeastern Tanzania and the tallest mountain in Africa, famed for its snow-capped summit rising above the surrounding savanna.
  • E. Mount Kenya
    Mount Kenya is an extinct stratovolcano in central Kenya and Africa’s second-highest mountain, renowned for its rugged peaks, glaciers, and alpine ecosystems.
  • 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_69a4940712c88190aa244f3fc6070a65 completed March 1, 2026, 7:31 p.m.
NER Named-entity recognition batch_69a4bbdea9b88190a88da718bf5c1897 completed March 1, 2026, 10:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69ac5eab1e6481908c175e175ae4743a completed March 7, 2026, 5:21 p.m.
Created at: March 1, 2026, 7:44 p.m.