Triple

T12945153
Position Surface form Disambiguated ID Type / Status
Subject Concepción E309740 entity
Predicate hasPublicTransport P1288 FINISHED
Object Biotrén E312286 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: Biotrén | Statement: [Concepción, hasPublicTransport, Biotrén]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Biotrén
Context triple: [Concepción, hasPublicTransport, Biotrén]
  • A. Biotrén chosen
    Biotrén is a suburban commuter rail system serving the Greater Concepción area in southern Chile.
  • B. Treniota
    Treniota was a 13th-century Lithuanian noble who briefly ruled as Grand Duke after orchestrating the assassination of his uncle, King Mindaugas, and leading pagan resistance against Christianization.
  • C. Megatren
    Megatren is the local name for Manila’s LRT Line 2, an elevated rapid transit line serving key east–west corridors in Metro Manila, Philippines.
  • D. Tsalka
    Tsalka is a town in southern Georgia known for its ethnically diverse population and its location near the Tsalka Reservoir in the Kvemo Kartli region.
  • E. Kogon
    Kogon is a small city in Uzbekistan known for its location near the historic center of Bukhara and its role as a local transport and industrial hub.
  • 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_69d7bdfb57a88190836b743e2825feca completed April 9, 2026, 2:55 p.m.
NER Named-entity recognition batch_69d97e1b3694819098527dcea3cfed93 completed April 10, 2026, 10:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f6af73e6348190be114e8c5ad181bf completed May 3, 2026, 2:14 a.m.
Created at: April 9, 2026, 5:43 p.m.