Triple

T19657959
Position Surface form Disambiguated ID Type / Status
Subject Michel Berger E471998 entity
Predicate notableWork P4 FINISHED
Object Seras-tu là ?
Seras-tu là ? is a popular French song by Michel Berger, known for its emotional lyrics and enduring status as a classic of French pop music.
 E1388159 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: Seras-tu là ? | Statement: [Michel Berger, notableWork, Seras-tu là ?]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Seras-tu là ?
Context triple: [Michel Berger, notableWork, Seras-tu là ?]
  • A. Ser.
    Ser. is the standard Latin abbreviation for the praenomen Servius, commonly used in ancient Roman naming conventions.
  • B. SERI
    SERI is Switzerland’s federal authority responsible for national policies and coordination in education, research, and innovation.
  • C. Seri
    The Seri are an Indigenous people of northwestern Mexico, traditionally living along the Gulf of California coast and known for their rich maritime culture, distinctive language, and artisanal crafts.
  • D. eta+Ser
    eta+Ser is the SIMBAD astronomical database identifier for Eta Serpentis, a K-type giant star in the constellation Serpens.
  • E. SEREB
    SEREB was a French aerospace company involved in the development of ballistic missiles and space launch vehicles before being merged into Aérospatiale.
  • 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: Seras-tu là ?
Triple: [Michel Berger, notableWork, Seras-tu là ?]
Generated description
Seras-tu là ? is a popular French song by Michel Berger, known for its emotional lyrics and enduring status as a classic of French pop music.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Seras-tu là ?
Target entity description: Seras-tu là ? is a popular French song by Michel Berger, known for its emotional lyrics and enduring status as a classic of French pop music.
  • A. Ser.
    Ser. is the standard Latin abbreviation for the praenomen Servius, commonly used in ancient Roman naming conventions.
  • B. SERI
    SERI is Switzerland’s federal authority responsible for national policies and coordination in education, research, and innovation.
  • C. Seri
    The Seri are an Indigenous people of northwestern Mexico, traditionally living along the Gulf of California coast and known for their rich maritime culture, distinctive language, and artisanal crafts.
  • D. eta+Ser
    eta+Ser is the SIMBAD astronomical database identifier for Eta Serpentis, a K-type giant star in the constellation Serpens.
  • E. SEREB
    SEREB was a French aerospace company involved in the development of ballistic missiles and space launch vehicles before being merged into Aérospatiale.
  • 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_69d8e51395348190ac1416d46dfc6db0 completed April 10, 2026, 11:54 a.m.
NER Named-entity recognition batch_69e641475f408190bd42ef3f8d590719 completed April 20, 2026, 3:07 p.m.
NED1 Entity disambiguation (via context triple) batch_6a077ef37b4c81908a2059b2db125c48 completed May 15, 2026, 8:15 p.m.
NEDg Description generation batch_6a0780b9b410819082b2e5e3d0e07534 completed May 15, 2026, 8:23 p.m.
NED2 Entity disambiguation (via description) batch_6a078242374481909a3c3dc27fb7d52c completed May 15, 2026, 8:29 p.m.
Created at: April 10, 2026, 1:45 p.m.