Triple

T27581073

Position	Surface form	Disambiguated ID	Type / Status
Subject	Thai Rak Thai Party	`E699586`	entity
Predicate	policy	`P172`	FINISHED
Object	one tambon one product program The One Tambon One Product (OTOP) program is a Thai government initiative that promotes locally made, community-based products from each subdistrict to stimulate rural entrepreneurship and economic development.	`E1780298`	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: one tambon one product program | Statement: [Thai Rak Thai Party, policy, one tambon one product program]

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: one tambon one product program
Triple: [Thai Rak Thai Party, policy, one tambon one product program]

Generated description

The One Tambon One Product (OTOP) program is a Thai government initiative that promotes locally made, community-based products from each subdistrict to stimulate rural entrepreneurship and economic development.

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_69ef6a4cb8b881909b3a8d630fd89df2`	completed	April 27, 2026, 1:53 p.m.
NER	Named-entity recognition	`batch_69f63015a6bc8190b4f47236dd4b6680`	completed	May 2, 2026, 5:10 p.m.
NED1	Entity disambiguation (via context triple)	`batch_6a12d0d5fa90819080d04891930d10e8`	completed	May 24, 2026, 10:20 a.m.
NEDg	Description generation	`batch_6a12d18be6088190a4662ba4460dfc84`	completed	May 24, 2026, 10:23 a.m.
NED2	Entity disambiguation (via description)	`batch_6a12d2747f6881909aa2a5b0c389a494`	completed	May 24, 2026, 10:27 a.m.

Created at: April 27, 2026, 2:02 p.m.