Triple

T36188489
Position Surface form Disambiguated ID Type / Status
Subject Lake Towuti E1046916 entity
Predicate hasInflow P967 FINISHED
Object Tominanga River
The Tominanga River is a watercourse in Sulawesi, Indonesia, that serves as a major tributary feeding Lake Towuti within the Malili Lake system.
 E2293826 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: Tominanga River | Statement: [Lake Towuti, hasInflow, Tominanga 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: Tominanga River
Triple: [Lake Towuti, hasInflow, Tominanga River]
Generated description
The Tominanga River is a watercourse in Sulawesi, Indonesia, that serves as a major tributary feeding Lake Towuti within the Malili Lake system.

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_69f76e3d4fbc81908c159c7beeb4ce00 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7b51662048190ab92048453eaa652 completed May 3, 2026, 8:50 p.m.
NED1 Entity disambiguation (via context triple) batch_6a7b09be4b6c81908fe478d36749588b completed Aug. 11, 2026, 11:38 a.m.
NEDg Description generation batch_6a7b0c3918f8819098de98dd1768d394 completed Aug. 11, 2026, 11:49 a.m.
NED2 Entity disambiguation (via description) batch_6a7b0dad120c8190a833dbdee538449d completed Aug. 11, 2026, 11:55 a.m.
Created at: May 3, 2026, 4:08 p.m.