Triple

T10837241
Position Surface form Disambiguated ID Type / Status
Subject Berta E255792 entity
Predicate alternativeName P39 FINISHED
Object Funj Berta E255792 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: Funj Berta | Statement: [Berta, alternativeName, Funj Berta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Funj Berta
Context triple: [Berta, alternativeName, Funj Berta]
  • A. Berta
    Berta is a fictional character in Paulo Coelho’s novel "The Devil and Miss Prym," serving as one of the villagers whose life and choices reflect the book’s central moral and spiritual dilemmas.
  • B. Berta
    Berta is the sharp-tongued, no-nonsense housekeeper known for her sarcastic humor on the sitcom "Two and a Half Men."
  • C. Berta chosen
    Berta is a Nilo-Saharan language spoken primarily in parts of Sudan and Ethiopia.
  • D. Baerbel
    Baerbel is a feminine given name of German origin, commonly used as an alternative spelling of Bärbel.
  • E. Frieda
    Frieda is a 1947 British drama film produced by Michael Balcon that explores post-World War II tensions and prejudice in England.
  • 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_69d6aa81a5d08190aa86689061d1ddd2 completed April 8, 2026, 7:20 p.m.
NER Named-entity recognition batch_69d746ff70148190b844ab92d796af6c completed April 9, 2026, 6:28 a.m.
NED1 Entity disambiguation (via context triple) batch_69deb12aae648190aa7c93cee60ae3ea completed April 14, 2026, 9:27 p.m.
Created at: April 8, 2026, 9:19 p.m.