Triple

T3997921
Position Surface form Disambiguated ID Type / Status
Subject LGV Nord E87141 entity
Predicate hasSignallingSystem P19148 FINISHED
Object TVM-300
TVM-300 is a high-speed railway cab signalling and train control system used on French high-speed lines such as the LGV Nord.
 E408790 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-300 | Statement: [LGV Nord, hasSignallingSystem, TVM-300]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: TVM-300
Context triple: [LGV Nord, hasSignallingSystem, TVM-300]
  • A. 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.
  • B. 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.
  • C. TX-38
    TX-38 is a U.S. congressional district in Texas represented in the House of Representatives.
  • D. TX-35
    TX-35 is the commonly used abbreviation for Texas's 35th congressional district, a U.S. House of Representatives district centered around parts of Austin and San Antonio.
  • 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-300
Triple: [LGV Nord, hasSignallingSystem, TVM-300]
Generated description
TVM-300 is a high-speed railway cab signalling and train control system used on French high-speed lines such as the LGV Nord.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: TVM-300
Target entity description: TVM-300 is a high-speed railway cab signalling and train control system used on French high-speed lines such as the LGV Nord.
  • A. 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.
  • B. 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.
  • C. TX-38
    TX-38 is a U.S. congressional district in Texas represented in the House of Representatives.
  • D. TX-35
    TX-35 is the commonly used abbreviation for Texas's 35th congressional district, a U.S. House of Representatives district centered around parts of Austin and San Antonio.
  • 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_69aed94118148190975e6aa4e554cde9 completed March 9, 2026, 2:29 p.m.
NER Named-entity recognition batch_69aefa3ef7ac8190abe02f440ff83c43 completed March 9, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69b5562139b481909faba39f4f36cd26 completed March 14, 2026, 12:35 p.m.
NEDg Description generation batch_69b5569d6b9c8190bea12fcfb9b444d9 completed March 14, 2026, 12:37 p.m.
NED2 Entity disambiguation (via description) batch_69b55706a2708190aeb41591b91f0fba completed March 14, 2026, 12:39 p.m.
Created at: March 9, 2026, 3:34 p.m.