Triple

T3715853
Position Surface form Disambiguated ID Type / Status
Subject D Line E81527 entity
Predicate connectsWith P37 FINISHED
Object K Line E81760 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: K Line | Statement: [D Line, connectsWith, K Line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: K Line
Context triple: [D Line, connectsWith, K Line]
  • A. K Line chosen
    K Line is a light rail route in the Los Angeles Metro Rail system serving neighborhoods in South Los Angeles and connecting to key transit hubs.
  • B. Hapag-Lloyd Express
    Hapag-Lloyd Express was a German low-cost airline that operated short-haul flights across Europe in the early 2000s before merging into TUIfly.
  • C. Franklin Line
    The Franklin Line is a Massachusetts Bay Transportation Authority (MBTA) commuter rail line serving communities southwest of Boston.
  • D. Kure Line
    The Kure Line is a Japanese railway line serving the coastal area around Kure in Hiroshima Prefecture, connecting local communities with larger urban centers.
  • E. Evergreen Marine
    Evergreen Marine is a major Taiwanese container shipping company known for operating one of the world’s largest fleets of container vessels.
  • 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_69ad8b1a81588190b3f27a5483bb610e completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adc9cf77dc819098979094172d82d1 completed March 8, 2026, 7:11 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4ce0f690c819091d9caf9271f9bbd completed March 14, 2026, 2:55 a.m.
Created at: March 8, 2026, 3:33 p.m.