Triple

T15057355
Position Surface form Disambiguated ID Type / Status
Subject Patna division E379529 entity
Predicate containsCity P294 FINISHED
Object Sasaram E304744 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: Sasaram | Statement: [Patna division, containsCity, Sasaram]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Sasaram
Context triple: [Patna division, containsCity, Sasaram]
  • A. Sasaram chosen
    Sasaram is a historic town in eastern India known for its grand Mughal-era monuments, especially the tomb of Sher Shah Suri.
  • B. Hajipur
    Hajipur is a prominent city in the Indian state of Bihar, known as an important railway and commercial hub located near the state capital, Patna.
  • C. Saharanpur
    Saharanpur is a city in the Indian state of Uttar Pradesh known as a commercial and transportation hub, particularly for its wood carving industry and agricultural trade.
  • D. Saharsa
    Saharsa is a city in the northeastern Indian state of Bihar, known as a major agricultural and commercial center in the Kosi river region.
  • E. Samastipur
    Samastipur is a city in the Indian state of Bihar known as an important railway junction and agricultural trade center in the region.
  • 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_69d85cd64d108190853797a95c11cc45 completed April 10, 2026, 2:13 a.m.
NER Named-entity recognition batch_69deda937f788190899d81bbb2084443 completed April 15, 2026, 12:23 a.m.
NED1 Entity disambiguation (via context triple) batch_69feae0fd7dc8190a10c8eb7542c3088 completed May 9, 2026, 3:46 a.m.
Created at: April 10, 2026, 3:01 a.m.