Triple

T12314519
Position Surface form Disambiguated ID Type / Status
Subject London fare zones E293566 entity
Predicate includesZone P6793 FINISHED
Object Zone 4
Zone 4 is a suburban travel zone in London’s public transport fare system, covering outer residential areas served by the Underground, Overground, and National Rail services.
 E980125 NE FINISHED

How this triple was built (4 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: Zone 4 | Statement: [London fare zones, includesZone, Zone 4]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zone 4
Context triple: [London fare zones, includesZone, Zone 4]
  • A. Zone 5
    Zone 5 is an outer fare zone in the London public transport system used for calculating ticket and Travelcard prices.
  • B. Zone 3
    Zone 3 is one of the MBTA Commuter Rail’s outer fare zones used to set ticket prices for trips between Boston and its surrounding suburbs.
  • C. Zone 3
    Zone 3 is one of the concentric public transport fare zones in the Île-de-France region surrounding central Paris.
  • D. Zone 3
    Zone 3 is a mid-distance public transport fare zone in London covering various suburban residential and commercial areas outside the city center.
  • E. Zone 2
    Zone 2 is a fare zone within a public transit system used to determine ticket prices and travel boundaries.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Zone 4
Triple: [London fare zones, includesZone, Zone 4]
Generated description
Zone 4 is a suburban travel zone in London’s public transport fare system, covering outer residential areas served by the Underground, Overground, and National Rail services.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zone 4
Target entity description: Zone 4 is a suburban travel zone in London’s public transport fare system, covering outer residential areas served by the Underground, Overground, and National Rail services.
  • A. Zone 5
    Zone 5 is an outer fare zone in the London public transport system used for calculating ticket and Travelcard prices.
  • B. Zone 3
    Zone 3 is one of the MBTA Commuter Rail’s outer fare zones used to set ticket prices for trips between Boston and its surrounding suburbs.
  • C. Zone 3
    Zone 3 is one of the concentric public transport fare zones in the Île-de-France region surrounding central Paris.
  • D. Zone 3
    Zone 3 is a mid-distance public transport fare zone in London covering various suburban residential and commercial areas outside the city center.
  • E. Zone 2
    Zone 2 is a fare zone within a public transit system used to determine ticket prices and travel boundaries.
  • F. None of above. chosen

Provenance (5 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_69d6ab6a2b50819082f6aedd32ed608a completed April 8, 2026, 7:24 p.m.
NER Named-entity recognition batch_69d93f03d3c88190baedffb83465bff8 completed April 10, 2026, 6:18 p.m.
NED1 Entity disambiguation (via context triple) batch_69f634688f548190b3c9013591da939b completed May 2, 2026, 5:29 p.m.
NEDg Description generation batch_69f6356b545c819089a5f5b901afc5f2 completed May 2, 2026, 5:33 p.m.
NED2 Entity disambiguation (via description) batch_69f636382ffc8190becfae41757a45d8 completed May 2, 2026, 5:36 p.m.
Created at: April 8, 2026, 9:53 p.m.