Triple

T10837549
Position Surface form Disambiguated ID Type / Status
Subject SEMAR E255801 entity
Predicate hasAbbreviation P43 FINISHED
Object SEMAR E255801 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: SEMAR | Statement: [SEMAR, hasAbbreviation, SEMAR]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SEMAR
Context triple: [SEMAR, hasAbbreviation, SEMAR]
  • A. SEMAR chosen
    SEMAR is the acronym for Mexico’s Secretariat of the Navy, the federal government department responsible for naval operations and maritime security.
  • B. Salatiga
    Salatiga is a small city in Central Java, Indonesia, known for its cool climate, educational institutions, and location between Mount Merbabu and Mount Telomoyo.
  • C. Blitar
    Blitar is a city in East Java, Indonesia, best known as the hometown and final resting place of the country’s first president, Sukarno.
  • D. Bima
    Bima is a coastal city on the eastern part of Sumbawa Island in Indonesia, known as a regional hub for trade and culture in West Nusa Tenggara.
  • E. Pakualaman
    Pakualaman is a small hereditary Javanese princely state and court within Yogyakarta, established in the 19th century as a minor parallel to the main sultanate.
  • 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_69d6aa81a5d08190aa86689061d1ddd2 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d747002b3081908726901ee83d8f38 completed April 9, 2026, 6:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69dff7c739708190b0d58fc2d6392c6c completed April 15, 2026, 8:40 p.m.
Created at: April 8, 2026, 9:19 p.m.