Triple

T3765961
Position Surface form Disambiguated ID Type / Status
Subject TGV POS E82676 entity
Predicate safetySystem P840 FINISHED
Object TVM-430
TVM-430 is a modern in-cab railway signaling and train protection system used on high-speed lines such as the French TGV network.
 E386838 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: TVM-430 | Statement: [TGV POS, safetySystem, TVM-430]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TVM-430
Context triple: [TGV POS, safetySystem, TVM-430]
  • A. Vanguard TV-4
    Vanguard TV-4 was the American Vanguard rocket that successfully launched Vanguard 1, one of the earliest artificial Earth satellites, during the early space race.
  • B. TX-38
    TX-38 is a U.S. congressional district in Texas represented in the House of Representatives.
  • C. TX-4
    TX-4 is the commonly used abbreviation for Texas's 4th congressional district in the United States House of Representatives.
  • D. TX-30
    TX-30 is the commonly used abbreviation for Texas's 30th congressional district, a U.S. House of Representatives district centered in the Dallas area.
  • E. TX-20
    TX-20 is a United States congressional district centered on San Antonio, Texas, known for its strong Democratic lean and significant Hispanic population.
  • 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: TVM-430
Triple: [TGV POS, safetySystem, TVM-430]
Generated description
TVM-430 is a modern in-cab railway signaling and train protection system used on high-speed lines such as the French TGV network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TVM-430
Target entity description: TVM-430 is a modern in-cab railway signaling and train protection system used on high-speed lines such as the French TGV network.
  • A. Vanguard TV-4
    Vanguard TV-4 was the American Vanguard rocket that successfully launched Vanguard 1, one of the earliest artificial Earth satellites, during the early space race.
  • B. TX-38
    TX-38 is a U.S. congressional district in Texas represented in the House of Representatives.
  • C. TX-4
    TX-4 is the commonly used abbreviation for Texas's 4th congressional district in the United States House of Representatives.
  • D. TX-30
    TX-30 is the commonly used abbreviation for Texas's 30th congressional district, a U.S. House of Representatives district centered in the Dallas area.
  • E. TX-20
    TX-20 is a United States congressional district centered on San Antonio, Texas, known for its strong Democratic lean and significant Hispanic population.
  • 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_69ad8b207b0081909d2b48843fbd8795 completed March 8, 2026, 2:43 p.m.
NER Named-entity recognition batch_69adcbfeb52081909c38103beb5dbdcd completed March 8, 2026, 7:20 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4e5221ab08190a3599afbbd5dbc6e completed March 14, 2026, 4:33 a.m.
NEDg Description generation batch_69b4e5f07c7081908e1aae715984aac4 completed March 14, 2026, 4:37 a.m.
NED2 Entity disambiguation (via description) batch_69b4e6531b48819083c0d14c2ca4f7c1 completed March 14, 2026, 4:38 a.m.
Created at: March 8, 2026, 3:35 p.m.