Triple

T9748498
Position Surface form Disambiguated ID Type / Status
Subject Umiam Lake E236376 entity
Predicate alsoKnownAs P39 FINISHED
Object Barapani E810129 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: Barapani | Statement: [Umiam Lake, alsoKnownAs, Barapani]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Barapani
Context triple: [Umiam Lake, alsoKnownAs, Barapani]
  • A. Barapani chosen
    Barapani is a scenic lakeside area in Meghalaya, India, known for the Umiam Lake reservoir and its proximity to Shillong.
  • B. Manikonda
    Manikonda is a rapidly developing residential and IT suburb in the western part of Hyderabad, known for its proximity to major technology hubs and corporate campuses.
  • C. Bahapur
    Bahapur is a locality in South Delhi, India, best known for housing the iconic Lotus Temple, a major Baháʼí House of Worship and architectural landmark.
  • D. Hogenakkal
    Hogenakkal is a small town in Tamil Nadu, India, known as the gateway to the famous Hogenakkal Falls on the Kaveri River.
  • E. Kotagiri
    Kotagiri is a hill station town in the Nilgiri district of Tamil Nadu, India, known for its cool climate, tea plantations, and scenic mountain landscapes.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 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_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9f68f8b88190b44babf5ae17dfef completed April 1, 2026, 10:42 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1bcd2e08c8190808b58fdabe0c9d3 completed April 5, 2026, 1:37 a.m.
Created at: March 30, 2026, 8:23 p.m.