Triple

T25263956
Position Surface form Disambiguated ID Type / Status
Subject Karachi road network E633379 entity
Predicate hasComponent P35 FINISHED
Object Malir Expressway
Malir Expressway is a major under-construction expressway in Karachi designed to provide a faster, signal-free route along the Malir River corridor and ease traffic congestion in the city.
E1675504 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: Malir Expressway | Statement: [Karachi road network, hasComponent, Malir Expressway]
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: Malir Expressway
Triple: [Karachi road network, hasComponent, Malir Expressway]
Generated description
Malir Expressway is a major under-construction expressway in Karachi designed to provide a faster, signal-free route along the Malir River corridor and ease traffic congestion in the city.

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_69e75a922ad481908f4f1f884583cb42 completed April 21, 2026, 11:08 a.m.
NER Named-entity recognition batch_69f48396bf9481909012e4ed818abfbc completed May 1, 2026, 10:42 a.m.
NED1 Entity disambiguation (via context triple) batch_6a1075d550b48190b194be7ac2c20ee6 completed May 22, 2026, 3:27 p.m.
NEDg Description generation batch_6a107744f43c81909026c2012da82a2d completed May 22, 2026, 3:33 p.m.
NED2 Entity disambiguation (via description) batch_6a1077e5ab8c8190b7e81764d7aacc72 completed May 22, 2026, 3:36 p.m.
Created at: April 21, 2026, 1:14 p.m.