Triple

T1736031
Position Surface form Disambiguated ID Type / Status
Subject Transilien E37920 entity
Predicate hasService P182 FINISHED
Object line P
Line P is a Transilien suburban rail line serving the eastern suburbs of the Paris metropolitan area.
 E195355 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: line P | Statement: [Transilien, hasService, line P]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: line P
Context triple: [Transilien, hasService, line P]
  • A. line N
    Line N is a Transilien suburban rail line serving the Paris region, connecting central Paris to western suburbs and towns.
  • B. line K
    Line K is a suburban rail line in the Île-de-France region of France, operating as part of the Transilien commuter train network serving Paris and its surrounding areas.
  • C. line H
    Line H is a suburban rail line in the Transilien network serving commuter routes in the northern suburbs of the Paris metropolitan area.
  • D. line R
    Line R is a suburban rail line in the Île-de-France region of France, operating as part of the Transilien network and connecting Paris with southeastern suburbs and towns.
  • E. line U
    Line U is a suburban rail line in the Île-de-France region of France, operating as part of the Transilien network to connect key destinations in the Paris metropolitan area.
  • 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: line P
Triple: [Transilien, hasService, line P]
Generated description
Line P is a Transilien suburban rail line serving the eastern suburbs of the Paris metropolitan area.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: line P
Target entity description: Line P is a Transilien suburban rail line serving the eastern suburbs of the Paris metropolitan area.
  • A. line N
    Line N is a Transilien suburban rail line serving the Paris region, connecting central Paris to western suburbs and towns.
  • B. line K
    Line K is a suburban rail line in the Île-de-France region of France, operating as part of the Transilien commuter train network serving Paris and its surrounding areas.
  • C. line H
    Line H is a suburban rail line in the Transilien network serving commuter routes in the northern suburbs of the Paris metropolitan area.
  • D. line R
    Line R is a suburban rail line in the Île-de-France region of France, operating as part of the Transilien network and connecting Paris with southeastern suburbs and towns.
  • E. line U
    Line U is a suburban rail line in the Île-de-France region of France, operating as part of the Transilien network to connect key destinations in the Paris metropolitan area.
  • 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_69a8861cc6ac8190ac0b2e31ccf62851 completed March 4, 2026, 7:21 p.m.
NER Named-entity recognition batch_69aa63a369048190bae352573f5082f1 completed March 6, 2026, 5:18 a.m.
NED1 Entity disambiguation (via context triple) batch_69ada0d999148190a889f761af05f431 completed March 8, 2026, 4:16 p.m.
NEDg Description generation batch_69ada207c50881909729bf565c2af9dd completed March 8, 2026, 4:21 p.m.
NED2 Entity disambiguation (via description) batch_69ada2c607fc819089d276ae9eca82a4 completed March 8, 2026, 4:24 p.m.
Created at: March 4, 2026, 7:30 p.m.