Triple

T21588363
Position Surface form Disambiguated ID Type / Status
Subject Temür Öljeytü E532711 entity
Predicate spouse P13 FINISHED
Object Bulugan
Bulugan was a Mongol noblewoman best known as the principal wife and empress consort of Yuan dynasty ruler Temür Öljeytü (Emperor Chengzong of Yuan).
 E1491070 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: Bulugan | Statement: [Temür Öljeytü, spouse, Bulugan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Bulugan
Context triple: [Temür Öljeytü, spouse, Bulugan]
  • A. Buk-gu
    Buk-gu is a northern administrative district of the metropolitan city of Ulsan in South Korea.
  • B. Buk-gu
    Buk-gu is a northern administrative district of Daegu, South Korea, known for its residential neighborhoods, educational institutions, and commercial areas.
  • C. Buk-gu
    Buk-gu is a northern district of Busan, South Korea, known as an urban residential and commercial area within the metropolitan city.
  • D. Buk-gu
    Buk-gu is a northern district of the coastal city of Pohang in South Korea, known for its industrial facilities and residential areas.
  • E. Sengan-en
    Sengan-en is a historic Japanese garden and former Shimazu clan villa in Kagoshima, renowned for its scenic views of Sakurajima and traditional landscape design.
  • 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: Bulugan
Triple: [Temür Öljeytü, spouse, Bulugan]
Generated description
Bulugan was a Mongol noblewoman best known as the principal wife and empress consort of Yuan dynasty ruler Temür Öljeytü (Emperor Chengzong of Yuan).
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Bulugan
Target entity description: Bulugan was a Mongol noblewoman best known as the principal wife and empress consort of Yuan dynasty ruler Temür Öljeytü (Emperor Chengzong of Yuan).
  • A. Buk-gu
    Buk-gu is a northern administrative district of the metropolitan city of Ulsan in South Korea.
  • B. Buk-gu
    Buk-gu is a northern district of the coastal city of Pohang in South Korea, known for its industrial facilities and residential areas.
  • C. Buk-gu
    Buk-gu is a northern administrative district of Daegu, South Korea, known for its residential neighborhoods, educational institutions, and commercial areas.
  • D. Buk-gu
    Buk-gu is a northern district of Busan, South Korea, known as an urban residential and commercial area within the metropolitan city.
  • E. Sengan-en
    Sengan-en is a historic Japanese garden and former Shimazu clan villa in Kagoshima, renowned for its scenic views of Sakurajima and traditional landscape design.
  • 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_69e0c46251648190876f0427cf2d321b completed April 16, 2026, 11:13 a.m.
NER Named-entity recognition batch_69eeeb621ab88190a33a943424ffb306 completed April 27, 2026, 4:51 a.m.
NED1 Entity disambiguation (via context triple) batch_6a09f67f08208190a1633fd2a28e2fa2 completed May 17, 2026, 5:10 p.m.
NEDg Description generation batch_6a09f6fc3d7081909edf8cc575b6293e completed May 17, 2026, 5:12 p.m.
NED2 Entity disambiguation (via description) batch_6a09f79098e48190acdcad615098c55a completed May 17, 2026, 5:14 p.m.
Created at: April 16, 2026, 6:31 p.m.