Triple

T16597987
Position Surface form Disambiguated ID Type / Status
Subject T4 (Athens tram) E403257 entity
Predicate hasLineNumber P11728 FINISHED
Object T4 E403257 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: T4 | Statement: [T4 (Athens tram), hasLineNumber, T4]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: T4
Context triple: [T4 (Athens tram), hasLineNumber, T4]
  • A. T4
    T4 is a light rail/tram line of the Trambesòs network serving the Barcelona metropolitan area.
  • B. T4 chosen
    T4 is one of the lines of the Athens tram system, providing urban light-rail service across part of the Athens metropolitan area.
  • C. T4
    T4 is a tram line serving the city of Villeurbanne as part of the Lyon metropolitan public transport network in France.
  • D. T4
    T4 is the large, modern main passenger terminal at Adolfo Suárez Madrid–Barajas Airport in Madrid, Spain, known for its distinctive architecture and extensive international flight operations.
  • E. T4
    T4 is the fourth passenger terminal at Melbourne Airport, serving as one of the airport’s main facilities for domestic and low-cost airline operations.
  • 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_69d883880d0c81908b5fcd454e767b60 completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e35d7367308190bf57b4a6a7bd33cc completed April 18, 2026, 10:31 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0075a226788190bfad73ffb6b32ec4 completed May 10, 2026, 12:10 p.m.
Created at: April 10, 2026, 5:16 a.m.