Triple

T30189187
Position Surface form Disambiguated ID Type / Status
Subject States Parties to the 2005 Convention E767425 entity
Predicate languageOfWork P15 FINISHED
Object UNESCO official languages
UNESCO official languages are the set of primary working languages used by UNESCO for its official communications, documentation, and proceedings.
E1903043 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: UNESCO official languages | Statement: [States Parties to the 2005 Convention, languageOfWork, UNESCO official languages]
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: UNESCO official languages
Triple: [States Parties to the 2005 Convention, languageOfWork, UNESCO official languages]
Generated description
UNESCO official languages are the set of primary working languages used by UNESCO for its official communications, documentation, and proceedings.

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_69f2247cc3d88190811dec3face94bf5 completed April 29, 2026, 3:32 p.m.
NER Named-entity recognition batch_69f67f828eb48190b0175709c9d92801 completed May 2, 2026, 10:49 p.m.
NED1 Entity disambiguation (via context triple) batch_6a275871f714819098c813eb39d4cea8 completed June 9, 2026, 12:04 a.m.
NEDg Description generation batch_6a275a43c69c8190b8b17520ed80530f completed June 9, 2026, 12:11 a.m.
NED2 Entity disambiguation (via description) batch_6a275b97a8b081909b579bb004a94f75 completed June 9, 2026, 12:17 a.m.
Created at: April 29, 2026, 7:28 p.m.