Triple

T35319063
Position Surface form Disambiguated ID Type / Status
Subject Lak Si District E1019985 entity
Predicate hasEducationalInstitution P113 FINISHED
Object Phranakhon Rajabhat University
Phranakhon Rajabhat University is a public higher education institution in Bangkok, Thailand, specializing in teacher education and community-focused academic programs.
E2182621 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: Phranakhon Rajabhat University | Statement: [Lak Si District, hasEducationalInstitution, Phranakhon Rajabhat University]
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: Phranakhon Rajabhat University
Triple: [Lak Si District, hasEducationalInstitution, Phranakhon Rajabhat University]
Generated description
Phranakhon Rajabhat University is a public higher education institution in Bangkok, Thailand, specializing in teacher education and community-focused academic programs.

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_69f76de9d45c81908a2ed0956b448b65 completed May 3, 2026, 3:46 p.m.
NER Named-entity recognition batch_69f79096a88081908cb64c02c31c72a0 completed May 3, 2026, 6:14 p.m.
NED1 Entity disambiguation (via context triple) batch_6a39b40f103481909bb4d5559cec83ea completed June 22, 2026, 10:15 p.m.
NEDg Description generation batch_6a39b53e39148190bb2509fdf8247452 completed June 22, 2026, 10:20 p.m.
NED2 Entity disambiguation (via description) batch_6a39bac115648190a52d68250532c1d8 completed June 22, 2026, 10:44 p.m.
Created at: May 3, 2026, 4:03 p.m.