Triple

T24354787
Position Surface form Disambiguated ID Type / Status
Subject Hunat Hatun Complex E613891 entity
Predicate hasPart P35 FINISHED
Object Hunat Hatun Madrasa
Hunat Hatun Madrasa is a 13th-century Seljuk Islamic school in Kayseri, Turkey, renowned for its stone architecture and role within the larger Hunat Hatun religious complex.
E1630470 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: Hunat Hatun Madrasa | Statement: [Hunat Hatun Complex, hasPart, Hunat Hatun Madrasa]
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: Hunat Hatun Madrasa
Triple: [Hunat Hatun Complex, hasPart, Hunat Hatun Madrasa]
Generated description
Hunat Hatun Madrasa is a 13th-century Seljuk Islamic school in Kayseri, Turkey, renowned for its stone architecture and role within the larger Hunat Hatun religious complex.

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_69e2d7ddd29481909e7f539a6072bd71 completed April 18, 2026, 1:01 a.m.
NER Named-entity recognition batch_69f29347e86881909cfbe5f23ce538b9 completed April 29, 2026, 11:24 p.m.
NED1 Entity disambiguation (via context triple) batch_6a0fd66a0b1481908928c6c437a75825 completed May 22, 2026, 4:07 a.m.
NEDg Description generation batch_6a0fd726b1b08190a13ac712e0d40a7a completed May 22, 2026, 4:10 a.m.
NED2 Entity disambiguation (via description) batch_6a0fd852ca8c81909bf1731a342d4a6d completed May 22, 2026, 4:15 a.m.
Created at: April 18, 2026, 1:59 a.m.