Triple

T15779262
Position Surface form Disambiguated ID Type / Status
Subject Lyon Metro line D E382568 entity
Predicate hasRollingStock P1305 FINISHED
Object MPL 85
MPL 85 is a type of rubber-tyred, automated metro train used on Lyon’s Line D.
E1177488 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: MPL 85 | Statement: [Lyon Metro line D, hasRollingStock, MPL 85]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: MPL 85
Context triple: [Lyon Metro line D, hasRollingStock, MPL 85]
  • A. MPL 75
    MPL 75 is a type of rubber-tyred, automated metro train used on the Lyon Metro system in France.
  • B. MPL
    MPL (Mozilla Public License) is a free and open-source software license created by Mozilla that allows code to be shared and modified while requiring that changes to MPL-covered files remain publicly available.
  • C. MPL
    MPL is the IATA airport code for Montpellier-Méditerranée Airport, serving the city of Montpellier in southern France.
  • D. MPL
    MPL is the National Rail station code for Marple railway station in Greater Manchester, England.
  • E. MPL 16
    MPL 16 is a modern automated rubber-tyred metro train used on Lyon’s Line B as part of the network’s upgraded driverless rolling stock.
  • 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: MPL 85
Triple: [Lyon Metro line D, hasRollingStock, MPL 85]
Generated description
MPL 85 is a type of rubber-tyred, automated metro train used on Lyon’s Line D.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: MPL 85
Target entity description: MPL 85 is a type of rubber-tyred, automated metro train used on Lyon’s Line D.
  • A. MPL 75
    MPL 75 is a type of rubber-tyred, automated metro train used on the Lyon Metro system in France.
  • B. MPL
    MPL is the National Rail station code for Marple railway station in Greater Manchester, England.
  • C. MPL
    MPL (Mozilla Public License) is a free and open-source software license created by Mozilla that allows code to be shared and modified while requiring that changes to MPL-covered files remain publicly available.
  • D. MPL
    MPL is the IATA airport code for Montpellier-Méditerranée Airport, serving the city of Montpellier in southern France.
  • E. MPL 16
    MPL 16 is a modern automated rubber-tyred metro train used on Lyon’s Line B as part of the network’s upgraded driverless rolling stock.
  • 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_69d86da09a10819082fe9797b23e4664 completed April 10, 2026, 3:25 a.m.
NER Named-entity recognition batch_69e053fea90081908e3fe4f91475bead completed April 16, 2026, 3:14 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff909f7f4481909cceb32e26af3780 completed May 9, 2026, 7:53 p.m.
NEDg Description generation batch_69ff93cf529c819097537d87689aad93 completed May 9, 2026, 8:06 p.m.
NED2 Entity disambiguation (via description) batch_69ff944ac4248190a3d60c2486910eaf completed May 9, 2026, 8:08 p.m.
Created at: April 10, 2026, 4:48 a.m.