Triple

T2043270
Position Surface form Disambiguated ID Type / Status
Subject LGV Sud-Est E44791 entity
Predicate alsoKnownAs P39 FINISHED
Object LN1
LN1 is the first French high-speed rail line (LGV Sud-Est), connecting Paris to Lyon and pioneering the TGV network.
 E227673 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: LN1 | Statement: [LGV Sud-Est, alsoKnownAs, LN1]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: LN1
Context triple: [LGV Sud-Est, alsoKnownAs, LN1]
  • A. LIN
    LIN is the three-letter IATA airport code for Milan Linate Airport, one of the main airports serving Milan, Italy.
  • B. LNS
    LNS is the commonly used abbreviation for the Laboratory for Nuclear Science, a research institution focused on advancing the understanding of nuclear and particle physics.
  • C. line N
    Line N is a Transilien suburban rail line serving the Paris region, connecting central Paris to western suburbs and towns.
  • D. RNLN
    RNLN is the abbreviation commonly used for the Royal Netherlands Navy, the maritime branch of the Dutch armed forces.
  • E. Lin
    Lin is a common Chinese surname shared by many individuals of Chinese and East Asian descent.
  • 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: LN1
Triple: [LGV Sud-Est, alsoKnownAs, LN1]
Generated description
LN1 is the first French high-speed rail line (LGV Sud-Est), connecting Paris to Lyon and pioneering the TGV network.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: LN1
Target entity description: LN1 is the first French high-speed rail line (LGV Sud-Est), connecting Paris to Lyon and pioneering the TGV network.
  • A. LIN
    LIN is the three-letter IATA airport code for Milan Linate Airport, one of the main airports serving Milan, Italy.
  • B. LNS
    LNS is the commonly used abbreviation for the Laboratory for Nuclear Science, a research institution focused on advancing the understanding of nuclear and particle physics.
  • C. line N
    Line N is a Transilien suburban rail line serving the Paris region, connecting central Paris to western suburbs and towns.
  • D. RNLN
    RNLN is the abbreviation commonly used for the Royal Netherlands Navy, the maritime branch of the Dutch armed forces.
  • E. Lin
    Lin is a common Chinese surname shared by many individuals of Chinese and East Asian descent.
  • 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_69a889159ec481908f9e4472d9f480c7 completed March 4, 2026, 7:33 p.m.
NER Named-entity recognition batch_69abb96f932881908bebfc4176fda7c0 completed March 7, 2026, 5:36 a.m.
NED1 Entity disambiguation (via context triple) batch_69ae1ffe98248190b6a4428c6c094d35 completed March 9, 2026, 1:18 a.m.
NEDg Description generation batch_69ae204fe6148190915219beb27128bc completed March 9, 2026, 1:20 a.m.
NED2 Entity disambiguation (via description) batch_69ae20d09c748190aebbfb88f0eedbaa completed March 9, 2026, 1:22 a.m.
Created at: March 4, 2026, 7:39 p.m.