Triple

T25887786
Position Surface form Disambiguated ID Type / Status
Subject Panshet Dam E652238 entity
Predicate reservoirName P13043 FINISHED
Object Tanajisagar Reservoir
Tanajisagar Reservoir is an artificial lake in Maharashtra, India, created by impounding the Ambi River to store water and support irrigation and drinking needs for the Pune region.
E1704117 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: Tanajisagar Reservoir | Statement: [Panshet Dam, reservoirName, Tanajisagar Reservoir]
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: Tanajisagar Reservoir
Triple: [Panshet Dam, reservoirName, Tanajisagar Reservoir]
Generated description
Tanajisagar Reservoir is an artificial lake in Maharashtra, India, created by impounding the Ambi River to store water and support irrigation and drinking needs for the Pune region.

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_69e7ab3b92cc81908febd90317862647 completed April 21, 2026, 4:52 p.m.
NER Named-entity recognition batch_69f603446aa88190852cc9bb25f30655 completed May 2, 2026, 1:59 p.m.
NED1 Entity disambiguation (via context triple) batch_6a1107680794819082c0902f3387d38e completed May 23, 2026, 1:48 a.m.
NEDg Description generation batch_6a11080f37c08190b1e814533c85f8f2 completed May 23, 2026, 1:51 a.m.
NED2 Entity disambiguation (via description) batch_6a1108aa194481908986a597992ffbac completed May 23, 2026, 1:53 a.m.
Created at: April 22, 2026, 8:18 a.m.