Triple

T36620889
Position Surface form Disambiguated ID Type / Status
Subject God’s Wife E904032 entity
Predicate notableOfficeHolder P5750 FINISHED
Object Maatkare Mutemhat
Maatkare Mutemhat was a prominent ancient Egyptian royal woman of the 21st Dynasty, best known for holding the powerful religious-political title of God’s Wife of Amun.
E2206717 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: Maatkare Mutemhat | Statement: [God’s Wife, notableOfficeHolder, Maatkare Mutemhat]
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: Maatkare Mutemhat
Triple: [God’s Wife, notableOfficeHolder, Maatkare Mutemhat]
Generated description
Maatkare Mutemhat was a prominent ancient Egyptian royal woman of the 21st Dynasty, best known for holding the powerful religious-political title of God’s Wife of Amun.

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_69f76e6ae750819096911e6e2d4d12c5 completed May 3, 2026, 3:48 p.m.
NER Named-entity recognition batch_69f7c4ace7b8819096462c6577fa11d1 completed May 3, 2026, 9:57 p.m.
NED1 Entity disambiguation (via context triple) batch_6a3e2c1724548190b753a32ba67e22b5 completed June 26, 2026, 7:36 a.m.
NEDg Description generation batch_6a3e2ce08a40819081db007321d0b279 completed June 26, 2026, 7:40 a.m.
NED2 Entity disambiguation (via description) batch_6a3e458016e881909cef925bc1bad341 completed June 26, 2026, 9:25 a.m.
Created at: May 3, 2026, 4:11 p.m.