Triple

T3684267
Position Surface form Disambiguated ID Type / Status
Subject Saint Prince Lazar E78186 entity
Predicate givenName P17 FINISHED
Object Lazar
Lazar is a masculine given name of Hebrew origin, commonly used in Slavic and other Eastern European cultures.
E380156 NE FINISHED

How this triple was built (4 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: Lazar | Statement: [Saint Prince Lazar, givenName, Lazar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Lazar
Context triple: [Saint Prince Lazar, givenName, Lazar]
  • A. Lazarus Salii
    Lazarus Salii was a Palauan politician who served as President of Palau during the late 1980s, playing a key role in the country’s early years of self-governance.
  • B. Lazare
    Lazare is a masculine given name of French origin, notably borne by the French mathematician, physicist, and statesman Lazare Carnot.
  • C. Łazarz
    Łazarz is a central district of Poznań, Poland, known for its historic urban architecture and major event venues.
  • D. Lazarus of Bethany
    Lazarus of Bethany is a New Testament figure whom Jesus famously raised from the dead, symbolizing resurrection and eternal life in Christian tradition.
  • E. Lázaro
    Lázaro is the baptismal name of You Heung-sik, a South Korean cardinal of the Catholic Church and Prefect of the Dicastery for the Clergy.
  • F. None of above. chosen
  • G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg Description generation gpt-5.1
Instruction
Generate a one-sentence description of the target entity. 
You are given a context triple in the form (subject, predicate, object), where the object is the target entity. 
# Instructions
Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. 
Avoid repeating the information from the triple, unless really essential.
# Response Format
Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Lazar
Triple: [Saint Prince Lazar, givenName, Lazar]
Generated description
Lazar is a masculine given name of Hebrew origin, commonly used in Slavic and other Eastern European cultures.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07
Target entity: Lazar
Target entity description: Lazar is a masculine given name of Hebrew origin, commonly used in Slavic and other Eastern European cultures.
  • A. Lazarus Salii
    Lazarus Salii was a Palauan politician who served as President of Palau during the late 1980s, playing a key role in the country’s early years of self-governance.
  • B. Lazare
    Lazare is a masculine given name of French origin, notably borne by the French mathematician, physicist, and statesman Lazare Carnot.
  • C. Łazarz
    Łazarz is a central district of Poznań, Poland, known for its historic urban architecture and major event venues.
  • D. Lazarus of Bethany
    Lazarus of Bethany is a New Testament figure whom Jesus famously raised from the dead, symbolizing resurrection and eternal life in Christian tradition.
  • E. Lázaro
    Lázaro is the baptismal name of You Heung-sik, a South Korean cardinal of the Catholic Church and Prefect of the Dicastery for the Clergy.
  • F. None of above. chosen

Provenance (5 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_69ad85e18c1c8190be8aafb227f39f48 completed March 8, 2026, 2:21 p.m.
NER Named-entity recognition batch_69adc49644d08190866d6c5df9d2b48d completed March 8, 2026, 6:48 p.m.
NED1 Entity disambiguation (via context triple) batch_69b4c3b86de88190bf4d39aae48ef930 completed March 14, 2026, 2:11 a.m.
NEDg Description generation batch_69b4c78bca688190bb06f64827285790 completed March 14, 2026, 2:27 a.m.
NED2 Entity disambiguation (via description) batch_69b4c7f33dec8190b71ea08cb1d34c32 completed March 14, 2026, 2:29 a.m.
Created at: March 8, 2026, 3:26 p.m.