Triple

T13679152
Position Surface form Disambiguated ID Type / Status
Subject Agen La Garenne Airport E327952 entity
Predicate serves P98 FINISHED
Object Agen E64826 NE FINISHED

How this triple was built (2 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: Agen | Statement: [Agen La Garenne Airport, serves, Agen]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agen
Context triple: [Agen La Garenne Airport, serves, Agen]
  • A. Agen chosen
    Agen is a historic town in southwestern France known for its prunes and location between Bordeaux and Toulouse.
  • B. Agen canton
    Agen canton is an administrative division in the Lot-et-Garonne department of southwestern France, centered around the city of Agen.
  • C. Agaja
    Agaja was an 18th-century king of the Kingdom of Dahomey in West Africa, known for expanding the kingdom’s power and centralizing its political and military structures.
  • D. Ageo
    Ageo is a city in Japan known as a residential and industrial hub within the Greater Tokyo metropolitan area.
  • E. Agutaynen
    Agutaynen is an Austronesian language spoken by the Agutaynen people in the Philippines, primarily in the province of Palawan.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69f7ce6254e88190b6b4541fa22dc508 completed May 3, 2026, 10:38 p.m.
Created at: April 9, 2026, 9:53 p.m.