Triple

T15465005
Position Surface form Disambiguated ID Type / Status
Subject Ditton Marsh E372008 entity
Predicate fareZone P844 FINISHED
Object Travelcard Zone 6 E301952 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: Travelcard Zone 6 | Statement: [Ditton Marsh, fareZone, Travelcard Zone 6]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Travelcard Zone 6
Context triple: [Ditton Marsh, fareZone, Travelcard Zone 6]
  • A. Travelcard Zone 6 chosen
    Travelcard Zone 6 is one of the outer fare zones in the London public transport system, covering suburban areas on the edge of Greater London.
  • B. Travelcard Zone 8
    Travelcard Zone 8 is one of the outer fare zones of the London public transport system, covering several suburban and commuter towns beyond the city’s central area.
  • C. Travelcard Zone 5
    Travelcard Zone 5 is an outer London public transport fare zone used to calculate ticket and Travelcard prices on services including the London Underground.
  • D. Travelcard Zone 7
    Travelcard Zone 7 is one of the outer fare zones of the London public transport system, covering several suburban and fringe areas beyond the central city.
  • E. Travelcard Zone 9
    Travelcard Zone 9 is one of the outermost London fare zones, covering certain suburban and out-of-London railway stations for Travelcard and contactless ticketing.
  • 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_69d85cc8bd308190886949510b42e764 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e03f680cec8190836a5ec841dee224 completed April 16, 2026, 1:46 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff2d01a23c819095cf75b7d5a801a9 completed May 9, 2026, 12:48 p.m.
Created at: April 10, 2026, 3:33 a.m.