Triple

T21556492
Position Surface form Disambiguated ID Type / Status
Subject Madrid Chamartín railway station E531904 entity
Predicate hasStationCode P1289 FINISHED
Object ESMCH 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: ESMCH | Statement: [Madrid Chamartín railway station, hasStationCode, ESMCH]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: ESMCH
Context triple: [Madrid Chamartín railway station, hasStationCode, ESMCH]
  • A. ESMCH chosen
    ESMCH is the station code used to identify Madrid Chamartín, one of the main railway stations in Madrid, Spain.
  • B. ESM
    ESM is the abbreviation for NASA’s Exceptional Service Medal, an honor awarded to individuals for significant, sustained contributions to the agency’s mission.
  • C. ESM
    ESM is an intergovernmental financial institution of the eurozone that provides financial assistance to member states in economic distress to safeguard financial stability.
  • D. EC-M
    EC-M is the vehicle registration and regional code assigned to the Macas area in Ecuador.
  • E. EMES
    EMES is the abbreviated name for the Europe and Middle East Section, an organizational division focused on activities and interests spanning those two regions.
  • 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_69e0c460232c81908de2c3819d17c00e completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eed2e04b048190ac3a9913094b4625 completed April 27, 2026, 3:07 a.m.
Created at: April 16, 2026, 6:29 p.m.