Triple
T10759028
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Netjerkare Siptah |
E253775
|
entity |
| Predicate | throneName |
P25582
|
FINISHED |
| Object | Netjerkare |
E634046
|
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: Netjerkare | Statement: [Netjerkare Siptah, throneName, Netjerkare]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Netjerkare Context triple: [Netjerkare Siptah, throneName, Netjerkare]
-
A.
Nynetjer
chosen
Nynetjer was an early Egyptian pharaoh of the Second Dynasty, known from archaeological and inscriptional evidence as a ruler during the formative period of the ancient Egyptian state.
-
B.
Netolicky
Netolicky is the surname of Bob Netolicky, an American former professional basketball player known for his career in the American Basketball Association (ABA).
-
C.
The Net
The Net is a 1995 techno-thriller film starring Sandra Bullock as a computer analyst whose identity is erased by cybercriminals, directed by Irwin Winkler.
-
D.
Inetkaes
Inetkaes was an ancient Egyptian royal woman, likely a princess of the early Old Kingdom period.
-
E.
Netishyn
Netishyn is a small city in western Ukraine known primarily for hosting the Khmelnytskyi Nuclear Power Plant.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6aa5f54f4819082d0bbcb6f8797e6 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d72ea21c5081908babc049d0330a75 |
completed | April 9, 2026, 4:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69dbdbc3780c819092337924e2ae90f8 |
completed | April 12, 2026, 5:52 p.m. |
Created at: April 8, 2026, 9:16 p.m.