Triple

T38619723
Position Surface form Disambiguated ID Type / Status
Subject Shehbaz Sharif E936834 entity
Predicate represents P129 FINISHED
Object NA-123 Lahore-V
NA-123 Lahore-V is a constituency of Pakistan’s National Assembly located in the Lahore district of Punjab.
 E2277630 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: NA-123 Lahore-V | Statement: [Shehbaz Sharif, represents, NA-123 Lahore-V]
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: NA-123 Lahore-V
Triple: [Shehbaz Sharif, represents, NA-123 Lahore-V]
Generated description
NA-123 Lahore-V is a constituency of Pakistan’s National Assembly located in the Lahore district of Punjab.

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_69f76ed403208190b862dc795171353f completed May 3, 2026, 3:50 p.m.
NER Named-entity recognition batch_69fcd979d1848190b62a18260f5e2bae completed May 7, 2026, 6:27 p.m.
NED1 Entity disambiguation (via context triple) batch_6a41f44afe4481908bb2140daf9b56bf completed June 29, 2026, 4:27 a.m.
NEDg Description generation batch_6a41f4d0afe4819093c6b93163cddfe5 completed June 29, 2026, 4:30 a.m.
NED2 Entity disambiguation (via description) batch_6a41f54e7d6c81909bac42ff59a0e27e completed June 29, 2026, 4:32 a.m.
Created at: May 3, 2026, 4:32 p.m.