Triple

T13607002
Position Surface form Disambiguated ID Type / Status
Subject Bep Voskuijl E325087 entity
Predicate employer P7 FINISHED
Object Opekta E296565 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: Opekta | Statement: [Bep Voskuijl, employer, Opekta]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Opekta
Context triple: [Bep Voskuijl, employer, Opekta]
  • A. Opekta chosen
    Opekta was a German-Dutch company that produced and sold pectin-based gelling agents for making jam, notably managed in its Amsterdam branch by Anne Frank’s father, Otto Frank.
  • B. Panazol
    Panazol is a suburban commune in west-central France, located just east of the city of Limoges in the Nouvelle-Aquitaine region.
  • C. Plaxtol
    Plaxtol is a small rural village and civil parish in Kent, England, known for its historic buildings and surrounding countryside.
  • D. Auscitain
    An Auscitain is a resident or native of Auch, a historic town in the Occitanie region of southwestern France.
  • E. Optax
    Optax is a gradient processing and optimization library for JAX, providing a flexible collection of composable optimizers and transformations for training machine learning models.
  • 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_69d80769eaf081909d82f44e484d6113 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb07e442c819086a8cbb967c03ad3 completed April 12, 2026, 2:47 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77f96280881908bab3af5c80f6d55 completed May 3, 2026, 5:02 p.m.
Created at: April 9, 2026, 9:50 p.m.