Triple

T1736027
Position Surface form Disambiguated ID Type / Status
Subject Transilien E37920 entity
Predicate hasService P182 FINISHED
Object line J
Line J is a suburban rail line in the Transilien network serving the western suburbs of Paris, particularly along the Paris–Saint-Lazare corridor.
 E194414 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 J | Statement: [Transilien, hasService, line J]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: line J
Context triple: [Transilien, hasService, line J]
  • A. LIN
    LIN is the three-letter IATA airport code for Milan Linate Airport, one of the main airports serving Milan, Italy.
  • B. Line H
    Line H is a rapid transit line of the Buenos Aires Underground that runs north–south, connecting key neighborhoods and serving as an important corridor in the city’s metro network.
  • C. JL
    JL is the IATA airline designator used for Japan Airlines, the flag carrier of Japan.
  • D. Line 5
    Line 5 is one of the main lines of the Santiago Metro in Chile, running across several key districts and serving as a major east–west transit corridor in the city.
  • E. Line 5
    Line 5 is a major north–south route of the Beijing Subway known for connecting key residential and commercial areas through the city center.
  • 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 J
Triple: [Transilien, hasService, line J]
Generated description
Line J is a suburban rail line in the Transilien network serving the western suburbs of Paris, particularly along the Paris–Saint-Lazare corridor.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: line J
Target entity description: Line J is a suburban rail line in the Transilien network serving the western suburbs of Paris, particularly along the Paris–Saint-Lazare corridor.
  • A. LIN
    LIN is the three-letter IATA airport code for Milan Linate Airport, one of the main airports serving Milan, Italy.
  • B. Line H
    Line H is a rapid transit line of the Buenos Aires Underground that runs north–south, connecting key neighborhoods and serving as an important corridor in the city’s metro network.
  • C. JL
    JL is the IATA airline designator used for Japan Airlines, the flag carrier of Japan.
  • D. Line 5
    Line 5 is a major north–south route of the Beijing Subway known for connecting key residential and commercial areas through the city center.
  • E. Line 5
    Line 5 is one of the main lines of the Santiago Metro in Chile, running across several key districts and serving as a major east–west transit corridor in the city.
  • 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_69ad8b008b7881909ac568af010bcf99 completed March 8, 2026, 2:43 p.m.
NEDg Description generation batch_69ad979dc1dc81908b64e57298ae6017 completed March 8, 2026, 3:37 p.m.
NED2 Entity disambiguation (via description) batch_69ad9836f4c8819098ba033b5f0d2a33 completed March 8, 2026, 3:39 p.m.
Created at: March 4, 2026, 7:30 p.m.