Triple

T31462999
Position Surface form Disambiguated ID Type / Status
Subject Maruf District E802653 entity
Predicate locatedIn P40 FINISHED
Object Greater Kandahar region
The Greater Kandahar region is a historical and strategic area in southern Afghanistan centered on the city of Kandahar, encompassing several surrounding districts and provinces.
E228127 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: Greater Kandahar region | Statement: [Maruf District, locatedIn, Greater Kandahar region]
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: Greater Kandahar region
Triple: [Maruf District, locatedIn, Greater Kandahar region]
Generated description
The Greater Kandahar region is a historical and strategic area in southern Afghanistan centered on the city of Kandahar, encompassing several surrounding districts and provinces.

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_69f348c84c1c81908739f100ecf7394e completed April 30, 2026, 12:19 p.m.
NER Named-entity recognition batch_69f6a14dcfac81909abcf2dc5f3f41ab completed May 3, 2026, 1:13 a.m.
NED1 Entity disambiguation (via context triple) batch_6a34da9c58c0819080f0fe0f1636ff8e completed June 19, 2026, 5:58 a.m.
NEDg Description generation batch_6a34dbcb8b508190b8bd72870246a160 completed June 19, 2026, 6:03 a.m.
NED2 Entity disambiguation (via description) batch_6a34dc4513c48190993300ccc4c2a6d4 completed June 19, 2026, 6:05 a.m.
Created at: April 30, 2026, 9:21 p.m.