Triple

T2647469
Position Surface form Disambiguated ID Type / Status
Subject Ain E53817 entity
Predicate containsTown P847 FINISHED
Object Nantua E280235 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: Nantua | Statement: [Ain, containsTown, Nantua]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Nantua
Context triple: [Ain, containsTown, Nantua]
  • A. Nantua chosen
    Nantua is a small town in eastern France known for its picturesque lake and surrounding Jura Mountains scenery.
  • B. Kivalina
    Kivalina is a small Inupiat village in northwestern Alaska known for its severe coastal erosion and vulnerability to climate change–driven sea level rise.
  • C. Takuu
    Takuu is a remote Polynesian outlier atoll near Papua New Guinea, known for its distinct Polynesian culture and language isolated within Melanesia.
  • D. Sugpiaq
    Sugpiaq are an Indigenous people of south-central Alaska, particularly Kodiak Island and the surrounding coastal regions, with a distinct Alutiiq language and maritime culture.
  • E. Gorely
    Gorely is an active stratovolcano complex on Russia’s Kamchatka Peninsula, known for its multiple craters, frequent eruptions, and striking acidic crater lakes.
  • 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_69ab495e192081909c77b622e8e7e15a completed March 6, 2026, 9:38 p.m.
NER Named-entity recognition batch_69abd919bf2c81908feb768f3391e985 completed March 7, 2026, 7:51 a.m.
NED1 Entity disambiguation (via context triple) batch_69afa050cc408190b2fa81a0f2da06eb completed March 10, 2026, 4:38 a.m.
Created at: March 6, 2026, 9:53 p.m.