Triple

T9167861
Position Surface form Disambiguated ID Type / Status
Subject Otar Iosseliani E220008 entity
Predicate givenName P17 FINISHED
Object Otar
Otar is a Georgian given name most notably borne by acclaimed film director Otar Iosseliani.
 E782372 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: Otar | Statement: [Otar Iosseliani, givenName, Otar]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Otar
Context triple: [Otar Iosseliani, givenName, Otar]
  • A. Oton
    Oton is a coastal municipality in the Philippine province of Iloilo known for its historical heritage and proximity to Iloilo City.
  • B. Ota
    Ota is a historically significant Awori town in southwestern Nigeria that has grown into a major industrial and educational hub.
  • C. Lavrans
    Lavrans is a central character in Sigrid Undset’s medieval Norwegian novel "Kristin Lavransdatter," known primarily as the devoted and principled father of the protagonist, Kristin.
  • D. Dror
    Dror was a Jewish underground resistance group associated with the Jewish Combat Organization that took part in anti-Nazi activities during World War II.
  • E. Paterva
    Paterva is a South African software company best known for creating Maltego, a powerful open-source intelligence and link analysis tool used in cybersecurity and digital investigations.
  • 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: Otar
Triple: [Otar Iosseliani, givenName, Otar]
Generated description
Otar is a Georgian given name most notably borne by acclaimed film director Otar Iosseliani.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Otar
Target entity description: Otar is a Georgian given name most notably borne by acclaimed film director Otar Iosseliani.
  • A. Oton
    Oton is a coastal municipality in the Philippine province of Iloilo known for its historical heritage and proximity to Iloilo City.
  • B. Ota
    Ota is a historically significant Awori town in southwestern Nigeria that has grown into a major industrial and educational hub.
  • C. Lavrans
    Lavrans is a central character in Sigrid Undset’s medieval Norwegian novel "Kristin Lavransdatter," known primarily as the devoted and principled father of the protagonist, Kristin.
  • D. Dror
    Dror was a Jewish underground resistance group associated with the Jewish Combat Organization that took part in anti-Nazi activities during World War II.
  • E. Paterva
    Paterva is a South African software company best known for creating Maltego, a powerful open-source intelligence and link analysis tool used in cybersecurity and digital investigations.
  • 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_69ca83e467108190abcae6a33b3d4dad completed March 30, 2026, 2:08 p.m.
NER Named-entity recognition batch_69ccaadfb50881909b9127f92e4b3e21 completed April 1, 2026, 5:19 a.m.
NED1 Entity disambiguation (via context triple) batch_69d05491ccec819093fcf2d764c5381b completed April 4, 2026, midnight
NEDg Description generation batch_69d05628d8708190a85437c5051a5a05 completed April 4, 2026, 12:07 a.m.
NED2 Entity disambiguation (via description) batch_69d056e4ad98819086e73edf15aa6210 completed April 4, 2026, 12:10 a.m.
Created at: March 30, 2026, 7:22 p.m.