Triple

T5225107
Position Surface form Disambiguated ID Type / Status
Subject Murillo E117965 entity
Predicate givenName P17 FINISHED
Object Bartolomé E459996 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: Bartolomé | Statement: [Murillo, givenName, Bartolomé]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Bartolomé
Context triple: [Murillo, givenName, Bartolomé]
  • A. Bartolomeo
    Bartolomeo is a masculine Italian given name historically borne by several notable figures, including artists, architects, and explorers.
  • B. Diego de los Ríos
    Diego de los Ríos was a Spanish general and colonial administrator best known as the last Spanish Governor-General of the Philippines during the final phase of Spanish rule in the archipelago.
  • C. Bartolomé Ordóñez chosen
    Bartolomé Ordóñez was a prominent early 16th-century Spanish sculptor of the Renaissance known for his refined marble tombs and religious works.
  • D. Martín de Osambela
    Martín de Osambela was a prominent Spanish merchant and landowner in colonial Lima, Peru, known for his wealth and influence in the late 18th and early 19th centuries.
  • E. Diego Gutiérrez
    Diego Gutiérrez is a former professional soccer midfielder and defender best known for his successful Major League Soccer career in the late 1990s and 2000s, particularly with the Chicago Fire.
  • 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_69bd4465e03081909bfcfd7113062590 completed March 20, 2026, 12:58 p.m.
NER Named-entity recognition batch_69bd7adb034c819086bf8a85fbf158f4 completed March 20, 2026, 4:50 p.m.
NED1 Entity disambiguation (via context triple) batch_69beeffc51888190938dc157b14c4b6c completed March 21, 2026, 7:22 p.m.
Created at: March 20, 2026, 1:48 p.m.