Triple

T19075148
Position Surface form Disambiguated ID Type / Status
Subject SEGIB E466884 entity
Predicate acronym P43 FINISHED
Object SEGIB NE NERFINISHED

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: SEGIB | Statement: [SEGIB, acronym, SEGIB]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: SEGIB
Context triple: [SEGIB, acronym, SEGIB]
  • A. SEGIB chosen
    SEGIB is the Ibero-American General Secretariat, an international organization that supports political, economic, and cultural cooperation among Spanish- and Portuguese-speaking countries in Europe and the Americas.
  • B. SIBF
    SIBF is the acronym for the Sharjah International Book Fair, one of the largest and most prominent literary and publishing events in the Arab world.
  • C. SIGI
    SIGI is a composite index developed by the OECD to measure and compare levels of gender-based discrimination in social institutions across countries.
  • D. GIB
    GIB is the IATA airport code for Gibraltar International Airport, which serves the British Overseas Territory of Gibraltar.
  • E. SEGU
    SEGU is the ICAO airport code for José Joaquín de Olmedo International Airport, the main air gateway serving Guayaquil, Ecuador.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69d8dd04f4488190b1121cc53ef2bfd6 completed April 10, 2026, 11:20 a.m.
NER Named-entity recognition batch_69e5e2e3c7b08190bf6448ead11ba916 completed April 20, 2026, 8:25 a.m.
Created at: April 10, 2026, 12:04 p.m.