Triple

T21285100
Position Surface form Disambiguated ID Type / Status
Subject Arthur Unger E524635 entity
Predicate hasFamilyName P18 FINISHED
Object Unger 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: Unger | Statement: [Arthur Unger, hasFamilyName, Unger]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Unger
Context triple: [Arthur Unger, hasFamilyName, Unger]
  • A. Unger chosen
    Unger is a German-origin surname borne by various notable individuals across fields such as politics, arts, and academia.
  • B. Unnan
    Unnan is a city in Shimane Prefecture, Japan, known for its rural landscapes, hot springs, and traditional cultural sites.
  • C. Utelle
    Utelle is a small rural commune in southeastern France, situated in the Alpes-Maritimes department in the Provence-Alpes-Côte d’Azur region.
  • D. Undy
    Undy is a village in Monmouthshire, Wales, situated near the town of Caldicot in the southeast of the country.
  • E. Unçaga
    Unçaga is an alternative spelling of the surname Unzaga, which is associated with several notable Spanish and Latin American historical figures.
  • 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_69e0b5171f6c8190a5d57201ede73811 completed April 16, 2026, 10:08 a.m.
NER Named-entity recognition batch_69e736d658e08190ad2f267123d53ede completed April 21, 2026, 8:35 a.m.
Created at: April 16, 2026, 4:03 p.m.