Triple

T34372697
Position Surface form Disambiguated ID Type / Status
Subject Ambassador of Gabon to Sweden E882203 entity
Predicate basedAt P40 FINISHED
Object Embassy of Gabon in Stockholm
The Embassy of Gabon in Stockholm is Gabon's official diplomatic mission in Sweden, handling political, consular, and cultural relations between the two countries.
E2093230 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 Gabon in Stockholm | Statement: [Ambassador of Gabon to Sweden, basedAt, Embassy of Gabon in Stockholm]
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 Gabon in Stockholm
Triple: [Ambassador of Gabon to Sweden, basedAt, Embassy of Gabon in Stockholm]
Generated description
The Embassy of Gabon in Stockholm is Gabon's official diplomatic mission in Sweden, handling political, consular, and cultural relations between the two countries.

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_69f349bf5d7481908dd5da4cbdf74047 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f7185163888190975562eb81431803 completed May 3, 2026, 9:41 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3704b1b2cc81909db821d65c8883cc completed June 20, 2026, 9:22 p.m.
NEDg Description generation batch_6a370577d8e08190848ce63a9865793d completed June 20, 2026, 9:26 p.m.
NED2 Entity disambiguation (via description) batch_6a370623291481909be4c2276969d415 completed June 20, 2026, 9:29 p.m.
Created at: May 1, 2026, 1:59 a.m.