Triple

T33971666
Position Surface form Disambiguated ID Type / Status
Subject Paris–Montpellier E871013 entity
Predicate mainTerminusStation P151408 FINISHED
Object Montpellier-Saint-Roch
Montpellier-Saint-Roch is the central railway station of Montpellier in southern France, serving as a major hub for regional and high-speed national train services.
 E2075327 NE FINISHED

How this triple was built (3 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: Montpellier-Saint-Roch | Statement: [Paris–Montpellier, mainTerminusStation, Montpellier-Saint-Roch]
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: Montpellier-Saint-Roch
Triple: [Paris–Montpellier, mainTerminusStation, Montpellier-Saint-Roch]
Generated description
Montpellier-Saint-Roch is the central railway station of Montpellier in southern France, serving as a major hub for regional and high-speed national train services.
PD Predicate disambiguation gpt-5-mini-2025-08-07 
Target predicate: mainTerminusStation
Context triple: [Paris–Montpellier, mainTerminusStation, Montpellier-Saint-Roch]
  • A. formerTerminalStation
    Indicates that a location once served as the end point (terminus) of a transportation line or route but no longer holds that status.
  • B. terminusStation
    Indicates that a station serves as the final endpoint or terminal stop for a given route or service.
  • C. railroadTerminusFor
    Indicates that one location serves as the end point or final station of a particular railroad line for another location.
  • D. railwayUpperTerminus
    Indicates that a railway line or route reaches its upper (typically higher-altitude or upstream) terminal endpoint at the related location.
  • E. railTerminusFor chosen
    Indicates that one location serves as the final or terminal rail station or endpoint for a specified rail line or service.
  • F. None of above.

Provenance (6 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_69f3499da0188190ab1a4ff06fb06a2a completed April 30, 2026, 12:22 p.m.
NER Named-entity recognition batch_69fb6fdc7eb081908ab8475efb38c430 completed May 6, 2026, 4:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3689ee8f24819085bebf1c7f866eb0 completed June 20, 2026, 12:39 p.m.
NEDg Description generation batch_6a368a76844c8190a7b85efae4d34fd5 completed June 20, 2026, 12:41 p.m.
NED2 Entity disambiguation (via description) batch_6a368b58c7848190b708ded1bbc44b60 completed June 20, 2026, 12:45 p.m.
PD Predicate disambiguation batch_69fb5a986e588190b7a10892bd2ff44c completed May 6, 2026, 3:13 p.m.
Created at: May 1, 2026, 1:50 a.m.