Triple

T30789017
Position Surface form Disambiguated ID Type / Status
Subject Achalpur E784038 entity
Predicate railwayLine P848 FINISHED
Object Murtajapur–Achalpur narrow-gauge line
The Murtajapur–Achalpur narrow-gauge line is a historic narrow-gauge railway in Maharashtra, India, known for its vintage trains and role in connecting rural communities in the region.
E1930422 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: Murtajapur–Achalpur narrow-gauge line | Statement: [Achalpur, railwayLine, Murtajapur–Achalpur narrow-gauge line]
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: Murtajapur–Achalpur narrow-gauge line
Triple: [Achalpur, railwayLine, Murtajapur–Achalpur narrow-gauge line]
Generated description
The Murtajapur–Achalpur narrow-gauge line is a historic narrow-gauge railway in Maharashtra, India, known for its vintage trains and role in connecting rural communities in the 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_69f224b2e2a48190b19aa43db9da5b67 completed April 29, 2026, 3:33 p.m.
NER Named-entity recognition batch_69f6900b856881908453d028f34c1e8f completed May 3, 2026, midnight
NED1 Entity disambiguation (via context triple) batch_6a28b0a96dc48190adcd89d30b24c7d0 completed June 10, 2026, 12:32 a.m.
NEDg Description generation batch_6a28b1cd9e78819093ff46123d12a12e completed June 10, 2026, 12:37 a.m.
NED2 Entity disambiguation (via description) batch_6a28b2b51afc81908cae8756f6d9b3a1 completed June 10, 2026, 12:41 a.m.
Created at: April 29, 2026, 8:41 p.m.