Triple

T23793837
Position Surface form Disambiguated ID Type / Status
Subject Paga Crocodile Pond E588478 entity
Predicate languageRegion P387 FINISHED
Object Kassena-Nankana area
The Kassena-Nankana area is a culturally rich region in northern Ghana inhabited mainly by Kassena and Nankana peoples, known for its traditional architecture, festivals, and historic sites.
E1600826 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: Kassena-Nankana area | Statement: [Paga Crocodile Pond, languageRegion, Kassena-Nankana area]
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: Kassena-Nankana area
Triple: [Paga Crocodile Pond, languageRegion, Kassena-Nankana area]
Generated description
The Kassena-Nankana area is a culturally rich region in northern Ghana inhabited mainly by Kassena and Nankana peoples, known for its traditional architecture, festivals, and historic sites.

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_69e25d15db58819092ac1e6791696fd9 completed April 17, 2026, 4:17 p.m.
NER Named-entity recognition batch_69f1c6da693481908194cbc9d6a0bfef completed April 29, 2026, 8:52 a.m.
NED1 Entity disambiguation (via context triple) batch_6a0f53f346e08190adb7e1af0f2ca21e completed May 21, 2026, 6:50 p.m.
NEDg Description generation batch_6a0f5634499c8190ac46621942ed2fd3 completed May 21, 2026, 7 p.m.
NED2 Entity disambiguation (via description) batch_6a0f56b2407c8190aa957702cf33267c completed May 21, 2026, 7:02 p.m.
Created at: April 17, 2026, 7:42 p.m.