Triple

T31741614
Position Surface form Disambiguated ID Type / Status
Subject Ashram Chowk E810155 entity
Predicate partOfCorridor P840 FINISHED
Object Delhi–Faridabad corridor
The Delhi–Faridabad corridor is a major urban transport route linking Delhi with the industrial city of Faridabad, facilitating heavy commuter and commercial traffic between the two.
 E1978917 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: Delhi–Faridabad corridor | Statement: [Ashram Chowk, partOfCorridor, Delhi–Faridabad corridor]
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: Delhi–Faridabad corridor
Triple: [Ashram Chowk, partOfCorridor, Delhi–Faridabad corridor]
Generated description
The Delhi–Faridabad corridor is a major urban transport route linking Delhi with the industrial city of Faridabad, facilitating heavy commuter and commercial traffic between the two.

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_69f348e233cc819083b6695f70cd75d8 completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6ab4924cc81909046f5cc06c146bf completed May 3, 2026, 1:56 a.m.
NED1 Entity disambiguation (via context triple) batch_6a2d9d4a8cf88190a63de2c9fd02076d completed June 13, 2026, 6:11 p.m.
NEDg Description generation batch_6a2da11bbd888190af24ca2b7ebf3269 completed June 13, 2026, 6:27 p.m.
NED2 Entity disambiguation (via description) batch_6a2e5767b5c08190b6ab769558da4220 completed June 14, 2026, 7:25 a.m.
Created at: April 30, 2026, 11:25 p.m.