Triple

T13610242
Position Surface form Disambiguated ID Type / Status
Subject Silverman E325168 entity
Predicate hasVariant P455 FINISHED
Object Silberman E734916 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: Silberman | Statement: [Silverman, hasVariant, Silberman]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Silberman
Context triple: [Silverman, hasVariant, Silberman]
  • A. Silberman chosen
    Silberman is a surname of likely Germanic or Jewish origin borne by various notable individuals, including jurists, artists, and public figures.
  • B. Tamir
    Tamir is a fictional character from the satirical comedy film "The Dictator," which stars Sacha Baron Cohen as a despotic North African leader.
  • C. Sigal
    Sigal was an ancient city that served as the political and administrative center of the Indo-Parthian kingdom in South Asia.
  • D. Zylberman
    Zylberman is a surname of likely Ashkenazi Jewish origin borne by individuals such as Bathsheba Zylberman.
  • E. Sigel
    Sigel is a surname most notably associated with American cinematographer Newton Thomas Sigel, known for his work on major Hollywood films.
  • 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_69d8076aae28819092cf636190ee5529 completed April 9, 2026, 8:09 p.m.
NER Named-entity recognition batch_69dbb0aa9a1481908c6f92495aff86c6 completed April 12, 2026, 2:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69f77f9a9f9c81909b0a8f4f51c461ae completed May 3, 2026, 5:02 p.m.
Created at: April 9, 2026, 9:50 p.m.