Triple

T26554217

Position	Surface form	Disambiguated ID	Type / Status
Subject	Suan Dok Gate	`E671758`	entity
Predicate	hasNearbyLandmark	`P2064`	FINISHED
Object	Wat Suan Dok Wat Suan Dok is a historic Buddhist temple in Chiang Mai, Thailand, known for its large chedi, royal burial stupas, and role as a center of Buddhist learning.	`E1733384`	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: Wat Suan Dok | Statement: [Suan Dok Gate, hasNearbyLandmark, Wat Suan Dok]

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: Wat Suan Dok
Triple: [Suan Dok Gate, hasNearbyLandmark, Wat Suan Dok]

Generated description

Wat Suan Dok is a historic Buddhist temple in Chiang Mai, Thailand, known for its large chedi, royal burial stupas, and role as a center of Buddhist learning.

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_69eeb32163f08190af5f81282738e27a`	completed	April 27, 2026, 12:51 a.m.
NER	Named-entity recognition	`batch_69f6146527d481908aae1bf455f32714`	completed	May 2, 2026, 3:12 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a11c81e26288190b027a84873d72943`	completed	May 23, 2026, 3:30 p.m.
NEDg	Description generation	`batch_6a11e5247fe88190bcd9a7afe016f3a2`	completed	May 23, 2026, 5:34 p.m.
NED2	Entity disambiguation (via description)	`batch_6a11e5f0744c8190904d781ba9d7e28c`	completed	May 23, 2026, 5:37 p.m.

Created at: April 27, 2026, 1:49 a.m.