Triple

T25692264
Position Surface form Disambiguated ID Type / Status
Subject Indian Coffee House, College Street E644230 entity
Predicate locatedIn P40 FINISHED
Object College Street
College Street is a historic thoroughfare in Kolkata, India, famed for its dense concentration of bookshops, educational institutions, and iconic coffee houses.
 E250559 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: College Street | Statement: [Indian Coffee House, College Street, locatedIn, College Street]
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: College Street
Triple: [Indian Coffee House, College Street, locatedIn, College Street]
Generated description
College Street is a historic thoroughfare in Kolkata, India, famed for its dense concentration of bookshops, educational institutions, and iconic coffee houses.

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_69e77e82c9bc8190893090b2f6c64f1d completed April 21, 2026, 1:41 p.m.
NER Named-entity recognition batch_69f5fbc1ad50819084657d6e4071e1f4 completed May 2, 2026, 1:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a42239e6a808190a71c40a29ed57cc6 completed June 29, 2026, 7:49 a.m.
NEDg Description generation batch_6a4224884dd48190b44b4bb02f147cc2 completed June 29, 2026, 7:53 a.m.
NED2 Entity disambiguation (via description) batch_6a422504b3ec8190a3c53e913edbc35b completed June 29, 2026, 7:55 a.m.
Created at: April 21, 2026, 8:29 p.m.