Triple

T7086337
Position Surface form Disambiguated ID Type / Status
Subject Banat E165084 entity
Predicate industrialCenter P11547 FINISHED
Object Reșița E640764 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: Reșița | Statement: [Banat, industrialCenter, Reșița]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Reșița
Context triple: [Banat, industrialCenter, Reșița]
  • A. Reșița chosen
    Reșița is an industrial city in western Romania, historically known as a major center of steel production and engineering in the Banat region.
  • B. Onești
    Onești is a town in Bacău County, Romania, best known internationally as the birthplace of legendary gymnast Nadia Comăneci.
  • C. Cernavodă
    Cernavodă is a town in southeastern Romania best known for its major Danube–Black Sea Canal port and the nearby Cernavodă Nuclear Power Plant.
  • D. Giulești
    Giulești is a residential neighborhood in western Bucharest, Romania, known for its working-class character and association with the Rapid București football club.
  • E. Săpânța
    Săpânța is a village in northern Romania renowned for its colorful and humorous "Merry Cemetery," a unique open-air museum of painted wooden crosses and epitaphs.
  • 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_69c6887d98408190912b9580666b0c1d completed March 27, 2026, 1:39 p.m.
NER Named-entity recognition batch_69c6e77415e88190ab65137382f1b155 completed March 27, 2026, 8:24 p.m.
NED1 Entity disambiguation (via context triple) batch_69c7ad7f7cac81909c13fbb60acd9a69 completed March 28, 2026, 10:29 a.m.
Created at: March 27, 2026, 2:41 p.m.