Triple

T15528026
Position Surface form Disambiguated ID Type / Status
Subject Spanish railway network E369132 entity
Predicate hasSignallingSystem P19148 FINISHED
Object ASFA E582705 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: ASFA | Statement: [Spanish railway network, hasSignallingSystem, ASFA]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ASFA
Context triple: [Spanish railway network, hasSignallingSystem, ASFA]
  • A. ASFA chosen
    ASFA is a Spanish railway automatic train protection system designed to monitor and control train speeds to enhance operational safety.
  • B. ASFA-Y
    ASFA-Y is an educational institution named after Princess Yennenga, often associated with secondary or technical schooling in Burkina Faso.
  • C. AFSA
    AFSA was a U.S. military signals intelligence and cryptologic organization that served as a predecessor to the National Security Agency (NSA).
  • D. AFAS
    AFAS is a regional agreement among ASEAN member states aimed at progressively liberalizing trade in services to enhance economic integration and competitiveness in Southeast Asia.
  • E. ASA
    ASA is the commonly used abbreviation for the Academy of Sciences of Albania, the country’s leading scientific research and advisory institution.
  • 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_69d85a1794cc8190b0b428716296e63e completed April 10, 2026, 2:01 a.m.
NER Named-entity recognition batch_69e0414620588190958ffde651ccab5f completed April 16, 2026, 1:54 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff3d598e6c8190870e9249197f5f53 completed May 9, 2026, 1:57 p.m.
Created at: April 10, 2026, 4:05 a.m.