Triple

T20559368
Position Surface form Disambiguated ID Type / Status
Subject ORWO film production E504804 entity
Predicate competesWith P1375 FINISHED
Object Agfa NE NERFINISHED

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: Agfa | Statement: [ORWO film production, competesWith, Agfa]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Agfa
Context triple: [ORWO film production, competesWith, Agfa]
  • A. Agfa chosen
    Agfa is a historic German company best known for its photographic films, cameras, and imaging technologies.
  • B. Fira
    Fira is a picturesque town on the Greek island of Santorini, known for its whitewashed buildings, cliffside views over the caldera, and vibrant tourism scene.
  • C. Gauda
    Gauda was a historic region in eastern India, centered in present-day West Bengal and Bangladesh, that served as an important political and cultural center in early medieval times.
  • D. Agadagba
    Agadagba is a town and local settlement located within the Ohimini area of Benue State, Nigeria.
  • E. Galafi
    Galafi is a small border town in Djibouti that serves as a key road crossing and trade gateway between Djibouti and Ethiopia.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (2 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_69e0b4b6587c8190aee63dc7cff244ea completed April 16, 2026, 10:06 a.m.
NER Named-entity recognition batch_69e6a5e178648190910795bae5422e50 completed April 20, 2026, 10:17 p.m.
Created at: April 16, 2026, 11:38 a.m.