Triple

T15550097
Position Surface form Disambiguated ID Type / Status
Subject Külüg Khan E370720 entity
Predicate personalName P24312 FINISHED
Object Khayishan
Khayishan, better known by his temple name Külüg Khan, was a Yuan dynasty emperor of Mongol China in the early 14th century.
 E1163237 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: Khayishan | Statement: [Külüg Khan, personalName, Khayishan]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07 
Target entity: Khayishan
Context triple: [Külüg Khan, personalName, Khayishan]
  • A. Kashshaya
    Kashshaya was a Neo-Babylonian royal woman known primarily as the wife of King Amel-Marduk.
  • B. Haisyn
    Haisyn is a city in central Ukraine known as a local administrative and economic center within Vinnytsia Oblast.
  • C. Khiyav
    Khiyav is the former and locally used name for Meshginshahr, a city in Ardabil Province in northwestern Iran.
  • D. Huvishka
    Huvishka was a prominent Kushan emperor of the 2nd century CE, known for his extensive coinage and role in consolidating the empire’s power and cultural diversity across Central and South Asia.
  • E. Hasbaya
    Hasbaya is a historic town in southern Lebanon known for its strategic location near Mount Hermon and its traditional Druze and Christian communities.
  • 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: Khayishan
Triple: [Külüg Khan, personalName, Khayishan]
Generated description
Khayishan, better known by his temple name Külüg Khan, was a Yuan dynasty emperor of Mongol China in the early 14th century.
NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07 
Target entity: Khayishan
Target entity description: Khayishan, better known by his temple name Külüg Khan, was a Yuan dynasty emperor of Mongol China in the early 14th century.
  • A. Kashshaya
    Kashshaya was a Neo-Babylonian royal woman known primarily as the wife of King Amel-Marduk.
  • B. Haisyn
    Haisyn is a city in central Ukraine known as a local administrative and economic center within Vinnytsia Oblast.
  • C. Khiyav
    Khiyav is the former and locally used name for Meshginshahr, a city in Ardabil Province in northwestern Iran.
  • D. Huvishka
    Huvishka was a prominent Kushan emperor of the 2nd century CE, known for his extensive coinage and role in consolidating the empire’s power and cultural diversity across Central and South Asia.
  • E. Hasbaya
    Hasbaya is a historic town in southern Lebanon known for its strategic location near Mount Hermon and its traditional Druze and Christian communities.
  • 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_69d85cc6cf40819091f4a5facee1ebe6 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69e04a93121881909d88ca55a39252ac completed April 16, 2026, 2:33 a.m.
NED1 Entity disambiguation (via context triple) batch_69ff455dfbcc8190a93e90c59b2d3045 completed May 9, 2026, 2:31 p.m.
NEDg Description generation batch_69ff467e5d6c8190ba3bf6557e683233 completed May 9, 2026, 2:36 p.m.
NED2 Entity disambiguation (via description) batch_69ff470912bc8190a42ee312aca55872 completed May 9, 2026, 2:39 p.m.
Created at: April 10, 2026, 4:08 a.m.