Triple

T22014175
Position Surface form Disambiguated ID Type / Status
Subject Lower Sloane Street E543662 entity
Predicate undergroundLineNearby P33788 FINISHED
Object Circle line 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: Circle line | Statement: [Lower Sloane Street, undergroundLineNearby, Circle line]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Circle line
Context triple: [Lower Sloane Street, undergroundLineNearby, Circle line]
  • A. Circle line chosen
    The Circle line is a central London Underground route forming a loop through key districts and interchanges in the city’s transport network.
  • B. Circle Line
    Circle Line is the alternative name for Seoul Subway Line 2, a major loop line encircling central Seoul and serving as one of the city's busiest metro routes.
  • C. Circle Line
    Circle Line is a mass rapid transit line in Singapore that forms a loop connecting key residential, commercial, and educational districts around the city.
  • D. Circular line
    The Circular line is a driverless rapid transit line in the Taipei Metro system that forms a loop connecting multiple districts around the city.
  • E. Circular line
    Circular line is the nickname for Line 6 of the Madrid Metro, a heavily used underground route that forms a loop around central Madrid.
  • 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_69e11e2db934819095556760c7d85e4d completed April 16, 2026, 5:36 p.m.
NER Named-entity recognition batch_69f127a6bf6081909865781ea7937a1b completed April 28, 2026, 9:33 p.m.
Created at: April 16, 2026, 8:22 p.m.