Triple
T22679920
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Apepi |
E560449
|
entity |
| Predicate | enemyOf |
P437
|
FINISHED |
| Object | Maat |
—
|
NE NERFINISHED |
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: Maat | Statement: [Apepi, enemyOf, Maat]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maat Context triple: [Apepi, enemyOf, Maat]
-
A.
Maat
chosen
Maat is the ancient Egyptian goddess and personification of truth, justice, balance, and cosmic order, central to both mythology and pharaonic law.
-
B.
Maatkare
Maatkare was the throne name of the ancient Egyptian pharaoh Hatshepsut, one of the most powerful and prominent female rulers of the New Kingdom.
-
C.
Thayet
Thayet is a town in central Myanmar known historically as an administrative and trading center along the Irrawaddy River.
-
D.
Yetu
Yetu is the protagonist of Nnedi Okorafor’s novella "The Deep," a memory-bearing historian of an underwater people descended from enslaved Africans thrown overboard.
-
E.
Qetesh
Qetesh is an ancient Near Eastern goddess associated with love, beauty, and fertility, who was later incorporated into the Egyptian pantheon.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e2454bfd00819099115715a22cb057 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1785f68b4819082c5570f741f5135 |
completed | April 29, 2026, 3:17 a.m. |
Created at: April 17, 2026, 3:11 p.m.