Triple

T726050
Position Surface form Disambiguated ID Type / Status
Subject Orlyval E14727 entity
Predicate hasStation P35 FINISHED
Object Orly 1-2-3
Orly 1-2-3 is a passenger station serving the Orlyval automated shuttle at Paris Orly Airport’s main terminal complex.
 E10908 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: Orly 1-2-3 | Statement: [Orlyval, hasStation, Orly 1-2-3]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Orly 1-2-3
Context triple: [Orlyval, hasStation, Orly 1-2-3]
  • A. Orly 3
    Orly 3 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for check-in, boarding, and arrivals operations.
  • B. Orly 2
    Orly 2 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for various domestic and international flights.
  • C. Orly 4
    Orly 4 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for various domestic and international flights.
  • D. Orly 1
    Orly 1 is one of the passenger terminals at Paris Orly Airport, serving as a key facility for check-in, boarding, and arrivals operations.
  • E. Orly
    Orly is a commune in the southern suburbs of Paris, France, best known for giving its name to the nearby Paris Orly Airport.
  • 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: Orly 1-2-3
Triple: [Orlyval, hasStation, Orly 1-2-3]
Generated description
Orly 1-2-3 is a passenger station serving the Orlyval automated shuttle at Paris Orly Airport’s main terminal complex.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Orly 1-2-3
Target entity description: Orly 1-2-3 is a passenger station serving the Orlyval automated shuttle at Paris Orly Airport’s main terminal complex.
  • A. Orly 3
    Orly 3 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for check-in, boarding, and arrivals operations.
  • B. Orly 2
    Orly 2 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for various domestic and international flights.
  • C. Orly 4
    Orly 4 is one of the main passenger terminals at Paris Orly Airport, serving as a hub for various domestic and international flights.
  • D. Orly 1 chosen
    Orly 1 is one of the passenger terminals at Paris Orly Airport, serving as a key facility for check-in, boarding, and arrivals operations.
  • E. Orly
    Orly is a commune in the southern suburbs of Paris, France, best known for giving its name to the nearby Paris Orly Airport.
  • F. None of above.

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_69a4934c753c81909b309027e48b9b3a completed March 1, 2026, 7:28 p.m.
NER Named-entity recognition batch_69a4a5a7fb7c819096db848fe2ba246a completed March 1, 2026, 8:46 p.m.
NED1 Entity disambiguation (via context triple) batch_69a65e3c22f08190b71734d9605a92f6 completed March 3, 2026, 4:06 a.m.
NEDg Description generation batch_69a65f0b2aa881909241216280abc51d completed March 3, 2026, 4:09 a.m.
NED2 Entity disambiguation (via description) batch_69a65faac11c8190bb2e015fc829e8da completed March 3, 2026, 4:12 a.m.
Created at: March 1, 2026, 7:37 p.m.