Triple

T16777541
Position Surface form Disambiguated ID Type / Status
Subject Johann Baptist von Spix E407765 entity
Predicate traveledTo P2694 FINISHED
Object Bahia E199233 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: Bahia | Statement: [Johann Baptist von Spix, traveledTo, Bahia]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bahia
Context triple: [Johann Baptist von Spix, traveledTo, Bahia]
  • A. Bahia
    Bahia is a traditional Brazilian football club based in Salvador, known for its passionate fanbase and historic success in national competitions.
  • B. Bahia chosen
    Bahia is a large and culturally rich state in northeastern Brazil, known for its Afro-Brazilian heritage, historic city of Salvador, and extensive Atlantic coastline.
  • C. Portuguesa State
    Portuguesa State is a landlocked agricultural region in western Venezuela known for its extensive plains and significant crop production, particularly of rice and corn.
  • D. Alagoas
    Alagoas is a small coastal state in northeastern Brazil known for its picturesque beaches, lagoons, and colonial-era history.
  • E. Maranhão
    Maranhão is a northeastern Brazilian state known for its colonial heritage, Afro-Brazilian culture, and the Lençóis Maranhenses dune and lagoon landscapes.
  • 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_69d8839270588190886720d9519bbf8f completed April 10, 2026, 4:58 a.m.
NER Named-entity recognition batch_69e3b212fc248190a8fe1124853bf16d completed April 18, 2026, 4:32 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00b283a9808190ba76110cf3c7f3a9 completed May 10, 2026, 4:29 p.m.
Created at: April 10, 2026, 5:22 a.m.