Triple

T30444458
Position Surface form Disambiguated ID Type / Status
Subject Chinatown, Ho Chi Minh City E774536 entity
Predicate hasTemple P1191 FINISHED
Object Nghia An Hoi Quan Pagoda
Nghia An Hoi Quan Pagoda is a historic Chinese-style temple in Ho Chi Minh City renowned for its ornate architecture and strong ties to the local Teochew community.
E1916796 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: Nghia An Hoi Quan Pagoda | Statement: [Chinatown, Ho Chi Minh City, hasTemple, Nghia An Hoi Quan Pagoda]
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: Nghia An Hoi Quan Pagoda
Triple: [Chinatown, Ho Chi Minh City, hasTemple, Nghia An Hoi Quan Pagoda]
Generated description
Nghia An Hoi Quan Pagoda is a historic Chinese-style temple in Ho Chi Minh City renowned for its ornate architecture and strong ties to the local Teochew community.

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_69f22493ef9c8190ae8c2afcb7f994c8 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f6869c15d48190be8870c750df12ed completed May 2, 2026, 11:19 p.m.
NED1 Entity disambiguation (via context triple) batch_6a27ac15ec1c8190913dc84f23dfb615 completed June 9, 2026, 6 a.m.
NEDg Description generation batch_6a27ad542b548190bf8286915784bf01 completed June 9, 2026, 6:06 a.m.
NED2 Entity disambiguation (via description) batch_6a27ade3972c8190b3bb7951caca8cc4 completed June 9, 2026, 6:08 a.m.
Created at: April 29, 2026, 8:08 p.m.