Triple

T10029843
Position Surface form Disambiguated ID Type / Status
Subject Taman Negara E204823 entity
Predicate highestPoint P210 FINISHED
Object Mount Tahan E210532 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 Tahan | Statement: [Taman Negara, highestPoint, Mount Tahan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Mount Tahan
Context triple: [Taman Negara, highestPoint, Mount Tahan]
  • A. Mount Tahan chosen
    Mount Tahan is the highest peak on the Malay Peninsula, located within Malaysia’s Taman Negara National Park and renowned for its challenging jungle treks.
  • B. Mount Welirang
    Mount Welirang is an active stratovolcano in East Java, Indonesia, known for its sulfur mining and frequent fumarolic activity.
  • C. Mount Pangasun
    Mount Pangasun is the tallest volcanic peak in the remote Babuyan Islands of the northern Philippines.
  • D. Mount Nanlaud
    Mount Nanlaud is the tallest mountain on the Micronesian island of Pohnpei, known for its lush tropical rainforest and frequent cloud cover.
  • E. Mount Dulang-dulang
    Mount Dulang-dulang is one of the highest and most biodiverse peaks in the Philippines, located in the northern part of Mindanao.
  • 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_69ca834d77188190ad645e33e8ca3200 completed March 30, 2026, 2:06 p.m.
NER Named-entity recognition batch_69cdcde69bd08190a5c79ec8487dfff6 completed April 2, 2026, 2:01 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2e559a1608190903e9b2dff12bb00 completed April 5, 2026, 10:42 p.m.
Created at: March 30, 2026, 8:54 p.m.