Triple

T34349477
Position Surface form Disambiguated ID Type / Status
Subject Taromenane E881523 entity
Predicate region P40 FINISHED
Object Orellana Province (Ecuador)
Orellana Province is an Amazonian region in northeastern Ecuador known for its vast rainforest, rich biodiversity, and the presence of indigenous groups such as the Taromenane.
E2093045 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: Orellana Province (Ecuador) | Statement: [Taromenane, region, Orellana Province (Ecuador)]
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: Orellana Province (Ecuador)
Triple: [Taromenane, region, Orellana Province (Ecuador)]
Generated description
Orellana Province is an Amazonian region in northeastern Ecuador known for its vast rainforest, rich biodiversity, and the presence of indigenous groups such as the Taromenane.

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_69f349bd06008190904c2f86c42749e3 completed April 30, 2026, 12:23 p.m.
NER Named-entity recognition batch_69f713f1da08819099ff53f6c2848726 completed May 3, 2026, 9:22 a.m.
NED1 Entity disambiguation (via context triple) batch_6a3704984ee48190ba5f5f757cb61683 completed June 20, 2026, 9:22 p.m.
NEDg Description generation batch_6a37058d9864819088afc4a2160ad876 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, 1:58 a.m.