Triple

T25222372
Position Surface form Disambiguated ID Type / Status
Subject Ayutthaya Historical Park E631997 entity
Predicate contains P35 FINISHED
Object Wihan Phra Mongkhon Bophit
Wihan Phra Mongkhon Bophit is a historic temple hall in Ayutthaya, Thailand, renowned for housing one of the largest bronze Buddha images in the country.
E1670826 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: Wihan Phra Mongkhon Bophit | Statement: [Ayutthaya Historical Park, contains, Wihan Phra Mongkhon Bophit]
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: Wihan Phra Mongkhon Bophit
Triple: [Ayutthaya Historical Park, contains, Wihan Phra Mongkhon Bophit]
Generated description
Wihan Phra Mongkhon Bophit is a historic temple hall in Ayutthaya, Thailand, renowned for housing one of the largest bronze Buddha images in the country.

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_69e75a8e0f688190a7aebe9a4815e25b completed April 21, 2026, 11:07 a.m.
NER Named-entity recognition batch_69f47cc0578881909ed1e40c09fdc38d completed May 1, 2026, 10:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1067d6187c81908194c0b4c3066713 completed May 22, 2026, 2:27 p.m.
NEDg Description generation batch_6a1068d5ff248190b9efb77366147c26 completed May 22, 2026, 2:31 p.m.
NED2 Entity disambiguation (via description) batch_6a1069d37ac88190ba6707f43e49f03c completed May 22, 2026, 2:36 p.m.
Created at: April 21, 2026, 1:03 p.m.