Triple

T26507339
Position Surface form Disambiguated ID Type / Status
Subject Urumea River E669582 entity
Predicate hasTributary P415 FINISHED
Object Añarbe River
The Añarbe River is a watercourse in the Basque Country of northern Spain that flows through Gipuzkoa and feeds into the Urumea River near San Sebastián.
 E1877648 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: Añarbe River | Statement: [Urumea River, hasTributary, Añarbe River]
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: Añarbe River
Triple: [Urumea River, hasTributary, Añarbe River]
Generated description
The Añarbe River is a watercourse in the Basque Country of northern Spain that flows through Gipuzkoa and feeds into the Urumea River near San Sebastián.

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_69eeb319ec70819090834c2591cf5f1e completed April 27, 2026, 12:51 a.m.
NER Named-entity recognition batch_69f6138e8f508190898b55c271967d95 completed May 2, 2026, 3:09 p.m.
NED1 Entity disambiguation (via context triple) batch_6a26613a6ce88190931f9f5f2521bae9 completed June 8, 2026, 6:29 a.m.
NEDg Description generation batch_6a26658b86e88190b68b3a7d183a72e9 completed June 8, 2026, 6:47 a.m.
NED2 Entity disambiguation (via description) batch_6a266c326a7081909d55ff20b5c3b851 completed June 8, 2026, 7:16 a.m.
Created at: April 27, 2026, 1:17 a.m.