Triple

T37207039
Position Surface form Disambiguated ID Type / Status
Subject Marine Lines railway station E922199 entity
Predicate near P350 FINISHED
Object Marine Lines neighborhood
Marine Lines neighborhood is a central South Mumbai locality along the city’s western coastline, known for its proximity to Marine Drive, commercial offices, and easy rail connectivity.
 E2216881 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: Marine Lines neighborhood | Statement: [Marine Lines railway station, near, Marine Lines neighborhood]
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: Marine Lines neighborhood
Triple: [Marine Lines railway station, near, Marine Lines neighborhood]
Generated description
Marine Lines neighborhood is a central South Mumbai locality along the city’s western coastline, known for its proximity to Marine Drive, commercial offices, and easy rail connectivity.

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_69f76ea4849481909b4a3073efb0114c completed May 3, 2026, 3:49 p.m.
NER Named-entity recognition batch_69fb366e6de881909e96076d05d7ae2d completed May 6, 2026, 12:39 p.m.
NED1 Entity disambiguation (via context triple) batch_6a4036213d2c81909e34733bba1e4556 completed June 27, 2026, 8:44 p.m.
NEDg Description generation batch_6a403692b8c0819080608b791a585931 completed June 27, 2026, 8:46 p.m.
NED2 Entity disambiguation (via description) batch_6a4037016aa881909a72d303ebec4756 completed June 27, 2026, 8:48 p.m.
Created at: May 3, 2026, 4:15 p.m.