Triple

T27794464
Position Surface form Disambiguated ID Type / Status
Subject Teshie (Ga) E701167 entity
Predicate traditionalAuthority P11127 FINISHED
Object Teshie Mantse (Ga chief)
Teshie Mantse (Ga chief) is the paramount traditional ruler and custodian of customs for the Ga people of Teshie in the Greater Accra Region of Ghana.
E1789336 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: Teshie Mantse (Ga chief) | Statement: [Teshie (Ga), traditionalAuthority, Teshie Mantse (Ga chief)]
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: Teshie Mantse (Ga chief)
Triple: [Teshie (Ga), traditionalAuthority, Teshie Mantse (Ga chief)]
Generated description
Teshie Mantse (Ga chief) is the paramount traditional ruler and custodian of customs for the Ga people of Teshie in the Greater Accra Region of Ghana.

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_69ef6a50d8088190acbf3dfbb06d8091 completed April 27, 2026, 1:53 p.m.
NER Named-entity recognition batch_69f6380a86648190b4ef3ee0ce498b12 completed May 2, 2026, 5:44 p.m.
NED1 Entity disambiguation (via context triple) batch_6a12eccc42608190bd45cec7b44fdbbe completed May 24, 2026, 12:19 p.m.
NEDg Description generation batch_6a12ee3f436c8190b1daf7ec5041304e completed May 24, 2026, 12:25 p.m.
NED2 Entity disambiguation (via description) batch_6a12eef54e2c8190b9e8d589f036b066 completed May 24, 2026, 12:28 p.m.
Created at: April 27, 2026, 5:30 p.m.