Triple

T5066900
Position Surface form Disambiguated ID Type / Status
Subject Lyon tramway line T4 E114165 entity
Predicate operator P179 FINISHED
Object TCL E487436 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: TCL | Statement: [Lyon tramway line T4, operator, TCL]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: TCL
Context triple: [Lyon tramway line T4, operator, TCL]
  • A. TCL chosen
    TCL is the public transport network operator serving Lyon and its metropolitan area in France, managing buses, trams, and metro services.
  • B. TCL Corporation
    TCL Corporation is a major Chinese electronics company best known globally for manufacturing televisions and other consumer electronics.
  • C. Huawei
    Huawei is a major Chinese multinational technology company best known globally for its telecommunications equipment, smartphones, and role in 5G network infrastructure.
  • D. Midea
    Midea is an important archaeological site in Greece that was a fortified citadel of the Mycenaean civilization.
  • E. ZTE
    ZTE is a major Chinese telecommunications and technology company known for manufacturing network equipment and smartphones and competing globally with firms like Nokia and Huawei.
  • 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_69bd443c0c8c81908663b77afb28e165 completed March 20, 2026, 12:57 p.m.
NER Named-entity recognition batch_69bd749bf69c819093e75dce56f1c0ab completed March 20, 2026, 4:23 p.m.
NED1 Entity disambiguation (via context triple) batch_69bea49d917081909ead17eed3f8af90 completed March 21, 2026, 2:01 p.m.
Created at: March 20, 2026, 1:38 p.m.