Triple

T13679303
Position Surface form Disambiguated ID Type / Status
Subject SU Agen Lot-et-Garonne E327956 entity
Predicate shortName P43 FINISHED
Object SU Agen
SU Agen is a French professional rugby union club based in Agen, known for its rich history and multiple national championship titles.
 E1055483 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: SU Agen | Statement: [SU Agen Lot-et-Garonne, shortName, SU Agen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: SU Agen
Context triple: [SU Agen Lot-et-Garonne, shortName, SU Agen]
  • A. SU
    SU was the two-letter country code used to represent the former Soviet Union in various international standards and systems.
  • B. SU
    SU is the commonly used abbreviation for Stockholm University, a major public research university in Stockholm, Sweden.
  • C. SU
    SU is the vehicle registration code for the Rhein-Sieg-Kreis district in the German state of North Rhine-Westphalia.
  • D. SU
    SU is the common abbreviation for Sofia University "St. Kliment Ohridski," Bulgaria’s oldest and most prestigious higher education institution.
  • E. SU
    SU is the IATA airline designator for Aeroflot, Russia’s flag carrier and largest airline.
  • 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: SU Agen
Triple: [SU Agen Lot-et-Garonne, shortName, SU Agen]
Generated description
SU Agen is a French professional rugby union club based in Agen, known for its rich history and multiple national championship titles.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: SU Agen
Target entity description: SU Agen is a French professional rugby union club based in Agen, known for its rich history and multiple national championship titles.
  • A. SU
    SU is the commonly used abbreviation for Stockholm University, a major public research university in Stockholm, Sweden.
  • B. SU
    SU was the two-letter country code used to represent the former Soviet Union in various international standards and systems.
  • C. SU
    SU is the vehicle registration code for the Rhein-Sieg-Kreis district in the German state of North Rhine-Westphalia.
  • D. SU
    SU is the common abbreviation for Sofia University "St. Kliment Ohridski," Bulgaria’s oldest and most prestigious higher education institution.
  • E. SU
    SU is the IATA airline designator for Aeroflot, Russia’s flag carrier and largest airline.
  • 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_69d8076f1fa8819094664a59b55010df completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbc66cbb088190907cb89dda8e4ebd completed April 12, 2026, 4:21 p.m.
NED1 Entity disambiguation (via context triple) batch_69f7944347a08190bc1386e78ddb3e71 completed May 3, 2026, 6:30 p.m.
NEDg Description generation batch_69f79523bf608190addeca563bea132e completed May 3, 2026, 6:34 p.m.
NED2 Entity disambiguation (via description) batch_69f7965cc9f88190acbf232615a9e87b completed May 3, 2026, 6:39 p.m.
Created at: April 9, 2026, 9:53 p.m.