Triple

T16898640
Position Surface form Disambiguated ID Type / Status
Subject Madrid public transport network E424375 entity
Predicate hasZone P6793 FINISHED
Object Zone E2
Zone E2 is an outer fare zone within the Madrid public transport system used to determine ticket prices and travel validity.
 E1239259 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 E2 | Statement: [Madrid public transport network, hasZone, Zone E2]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Zone E2
Context triple: [Madrid public transport network, hasZone, Zone E2]
  • A. Zone E
    Zone E is a Metra commuter rail fare zone in the Chicago metropolitan area used to determine ticket prices based on distance traveled.
  • 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 E2
Triple: [Madrid public transport network, hasZone, Zone E2]
Generated description
Zone E2 is an outer fare zone within the Madrid public transport system used to determine ticket prices and travel validity.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Zone E2
Target entity description: Zone E2 is an outer fare zone within the Madrid public transport system used to determine ticket prices and travel validity.
  • A. Zone E
    Zone E is a Metra commuter rail fare zone in the Chicago metropolitan area used to determine ticket prices based on distance traveled.
  • 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 a mid-distance public transport fare zone in London covering various suburban residential and commercial areas outside the city center.
  • D. Zone 3
    Zone 3 is one of the concentric public transport fare zones in the Île-de-France region surrounding central Paris.
  • 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_69d889da3e8c8190a2b118f383f0beac completed April 10, 2026, 5:25 a.m.
NER Named-entity recognition batch_69e3c8da7b0481909111358871875023 completed April 18, 2026, 6:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00c7b0783c81909c87de503d5e7e3c completed May 10, 2026, 6 p.m.
NEDg Description generation batch_6a00c830f7ac8190ae25232f88e9774b completed May 10, 2026, 6:02 p.m.
NED2 Entity disambiguation (via description) batch_6a00c8aa5aac8190be5f79f992c8a0ec completed May 10, 2026, 6:04 p.m.
Created at: April 10, 2026, 5:29 a.m.