Triple

T8475981
Position Surface form Disambiguated ID Type / Status
Subject Paris Métro Line 7 E200393 entity
Predicate servesStation P839 FINISHED
Object Opéra
Opéra is a major Paris Métro station and transport hub located near the Palais Garnier in central Paris.
 E735511 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: Opéra | Statement: [Paris Métro Line 7, servesStation, Opéra]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Opéra
Context triple: [Paris Métro Line 7, servesStation, Opéra]
  • A. Ópera
    Ópera is a central Madrid Metro station located near the historic Teatro Real opera house and Plaza de Oriente.
  • B. OPERA
    OPERA was a long-baseline neutrino oscillation experiment at the Gran Sasso National Laboratory in Italy, designed to detect tau neutrinos in a beam sent from CERN.
  • C. Opera
    Opera is a web browser known for its built-in features like a free VPN, ad blocker, and integrated messaging tools.
  • D. Opera
    Opera is a metro station on Cairo's Line 2 serving the downtown area near the Cairo Opera House and surrounding cultural landmarks.
  • E. Kroll Opera
    Kroll Opera was a progressive Berlin opera house in the Weimar Republic known for its innovative, avant-garde productions and association with prominent conductors like Otto Klemperer.
  • 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: Opéra
Triple: [Paris Métro Line 7, servesStation, Opéra]
Generated description
Opéra is a major Paris Métro station and transport hub located near the Palais Garnier in central Paris.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Opéra
Target entity description: Opéra is a major Paris Métro station and transport hub located near the Palais Garnier in central Paris.
  • A. Ópera
    Ópera is a central Madrid Metro station located near the historic Teatro Real opera house and Plaza de Oriente.
  • B. OPERA
    OPERA was a long-baseline neutrino oscillation experiment at the Gran Sasso National Laboratory in Italy, designed to detect tau neutrinos in a beam sent from CERN.
  • C. Opera
    Opera is a web browser known for its built-in features like a free VPN, ad blocker, and integrated messaging tools.
  • D. Opera
    Opera is a metro station on Cairo's Line 2 serving the downtown area near the Cairo Opera House and surrounding cultural landmarks.
  • E. Kroll Opera
    Kroll Opera was a progressive Berlin opera house in the Weimar Republic known for its innovative, avant-garde productions and association with prominent conductors like Otto Klemperer.
  • 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_69ca831b17988190a1f3f3413d57b820 completed March 30, 2026, 2:05 p.m.
NER Named-entity recognition batch_69cbe51e21548190811e3c7ba7b196e5 completed March 31, 2026, 3:15 p.m.
NED1 Entity disambiguation (via context triple) batch_69ce3a0f5e088190b70b2c7437884b3b completed April 2, 2026, 9:42 a.m.
NEDg Description generation batch_69ce3b1f6f7c8190927b5e2684ae207b completed April 2, 2026, 9:47 a.m.
NED2 Entity disambiguation (via description) batch_69ce3b9d38e081909be10cb209b15427 completed April 2, 2026, 9:49 a.m.
Created at: March 30, 2026, 6:12 p.m.