Triple

T4903358
Position Surface form Disambiguated ID Type / Status
Subject Monte San Clemente E109854 entity
Predicate alsoKnownAs P39 FINISHED
Object Monte San Valentín E19728 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: Monte San Valentín | Statement: [Monte San Clemente, alsoKnownAs, Monte San Valentín]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Monte San Valentín
Context triple: [Monte San Clemente, alsoKnownAs, Monte San Valentín]
  • A. Monte San Valentín chosen
    Monte San Valentín is a prominent glaciated mountain in Chilean Patagonia and the region’s highest summit, known for its remote location and challenging climbing conditions.
  • B. Cerro San Valentín
    Cerro San Valentín is the highest mountain in Chilean Patagonia, rising prominently within the Northern Patagonian Ice Field of southern Chile.
  • C. Monte Granero
    Monte Granero is a prominent mountain peak in the western Italian Alps, known for its rugged terrain and scenic alpine landscapes.
  • D. Monte Renoso
    Monte Renoso is a prominent mountain in southern Corsica, France, known for its rugged terrain and scenic alpine landscapes.
  • E. Monte Toro
    Monte Toro is the tallest mountain on the Spanish island of Menorca, known for its panoramic views and a sanctuary at its summit.
  • 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_69bd441180708190ba42ffb44fea533a completed March 20, 2026, 12:56 p.m.
NER Named-entity recognition batch_69bd6e6fdeac81909092f51ae40ad20e completed March 20, 2026, 3:57 p.m.
NED1 Entity disambiguation (via context triple) batch_69be89e3215081908120a8d307debbb3 completed March 21, 2026, 12:06 p.m.
Created at: March 20, 2026, 1:28 p.m.