Triple

T35545587
Position Surface form Disambiguated ID Type / Status
Subject Bang Rak District E1027202 entity
Predicate hasEmbassy P12096 FINISHED
Object Embassy of Belgium in Thailand
The Embassy of Belgium in Thailand is Belgium's official diplomatic mission in Bangkok, providing consular services and managing political, economic, and cultural relations between Belgium and Thailand.
E2145331 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: Embassy of Belgium in Thailand | Statement: [Bang Rak District, hasEmbassy, Embassy of Belgium in Thailand]
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: Embassy of Belgium in Thailand
Triple: [Bang Rak District, hasEmbassy, Embassy of Belgium in Thailand]
Generated description
The Embassy of Belgium in Thailand is Belgium's official diplomatic mission in Bangkok, providing consular services and managing political, economic, and cultural relations between Belgium and Thailand.

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_69f76e008ba08190927acd8e5e0344c8 completed May 3, 2026, 3:47 p.m.
NER Named-entity recognition batch_69f798098c488190ac1c85d8b5c7a90a completed May 3, 2026, 6:46 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3852ed67b48190955306f1a45c7b9a completed June 21, 2026, 9:09 p.m.
NEDg Description generation batch_6a38537cefd48190b5d223a5506b4f2d completed June 21, 2026, 9:11 p.m.
NED2 Entity disambiguation (via description) batch_6a38546765988190bba6f0bc046274df completed June 21, 2026, 9:15 p.m.
Created at: May 3, 2026, 4:04 p.m.