Triple

T16983990
Position Surface form Disambiguated ID Type / Status
Subject Alberto Ammann E412014 entity
Predicate givenName P17 FINISHED
Object Alberto E65543 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: Alberto | Statement: [Alberto Ammann, givenName, Alberto]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Alberto
Context triple: [Alberto Ammann, givenName, Alberto]
  • A. Alberto chosen
    Alberto is a masculine given name common in Romance-language countries, derived from the Germanic name Albert and sharing its meaning of "noble" or "bright."
  • B. Roberto
    Roberto is a masculine given name commonly used in Romance-language countries, equivalent to the English name Robert.
  • C. Leonardo Antonelli
    Leonardo Antonelli was an Italian cardinal of the Roman Catholic Church who served as Dean of the College of Cardinals around the turn of the 19th century.
  • D. Renato
    Renato is a masculine given name of Latin origin, commonly used in Italian, Portuguese, and Spanish-speaking countries.
  • E. Hércules Barsotti
    Hércules Barsotti was a Brazilian artist known for his contributions to geometric abstraction and concrete art in the mid-20th century.
  • 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_69d886ca8f348190812768ea8d5055ce completed April 10, 2026, 5:12 a.m.
NER Named-entity recognition batch_69e3d18a0bf881908c449f499eb86495 completed April 18, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a00dc0f13c88190b55da5be40a0a476 completed May 10, 2026, 7:27 p.m.
Created at: April 10, 2026, 5:32 a.m.