Triple

T10085181
Position Surface form Disambiguated ID Type / Status
Subject BB 7200 E215200 entity
Predicate manufacturer P490 FINISHED
Object MTE
MTE is a French company known for manufacturing the BB 7200 class of electric locomotives for the French railways.
 E841049 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: MTE | Statement: [BB 7200, manufacturer, MTE]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: MTE
Context triple: [BB 7200, manufacturer, MTE]
  • A. MeTC
    MeTC is the standard abbreviation for the Metropolitan Trial Courts, which are first-level courts in the Philippines that handle minor civil and criminal cases within metropolitan areas.
  • B. NMT
    NMT is a science and engineering-focused public research university located in Socorro, New Mexico, known for its strong programs in mining, engineering, and the physical sciences.
  • C. MHT
    MHT is the IATA airport code for Manchester–Boston Regional Airport, a commercial airport serving the Manchester, New Hampshire area and the greater Boston region.
  • D. .mt
    .mt is the country code top-level domain (ccTLD) assigned to Malta for use on the internet.
  • E. TMTA
    TMTA was the stock ticker symbol for Transmeta Corporation, a now-defunct American semiconductor company known for its low-power x86-compatible microprocessors.
  • 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: MTE
Triple: [BB 7200, manufacturer, MTE]
Generated description
MTE is a French company known for manufacturing the BB 7200 class of electric locomotives for the French railways.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: MTE
Target entity description: MTE is a French company known for manufacturing the BB 7200 class of electric locomotives for the French railways.
  • A. MeTC
    MeTC is the standard abbreviation for the Metropolitan Trial Courts, which are first-level courts in the Philippines that handle minor civil and criminal cases within metropolitan areas.
  • B. NMT
    NMT is a science and engineering-focused public research university located in Socorro, New Mexico, known for its strong programs in mining, engineering, and the physical sciences.
  • C. MHT
    MHT is the IATA airport code for Manchester–Boston Regional Airport, a commercial airport serving the Manchester, New Hampshire area and the greater Boston region.
  • D. .mt
    .mt is the country code top-level domain (ccTLD) assigned to Malta for use on the internet.
  • E. TMTA
    TMTA was the stock ticker symbol for Transmeta Corporation, a now-defunct American semiconductor company known for its low-power x86-compatible microprocessors.
  • 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_69ca83a1eed081908b2e9580f2ebeea7 completed March 30, 2026, 2:07 p.m.
NER Named-entity recognition batch_69cdd04609748190987a9364a387fa61 completed April 2, 2026, 2:11 a.m.
NED1 Entity disambiguation (via context triple) batch_69d2b68188c48190ac783cdfc072c502 completed April 5, 2026, 7:22 p.m.
NEDg Description generation batch_69d2b7aecdb081909f651c1bc1bcfd75 completed April 5, 2026, 7:27 p.m.
NED2 Entity disambiguation (via description) batch_69d2b86bf8948190a79046efadc4adea completed April 5, 2026, 7:30 p.m.
Created at: March 30, 2026, 9 p.m.