Triple
T4935605
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abimelech |
E110803
|
entity |
| Predicate | diedAt |
P21
|
FINISHED |
| Object | Thebez |
E481022
|
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: Thebez | Statement: [Abimelech, diedAt, Thebez]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thebez Context triple: [Abimelech, diedAt, Thebez]
-
A.
Thebez
chosen
Thebez was an ancient Canaanite town mentioned in the Hebrew Bible, notable as the site where Abimelech was fatally injured when a woman dropped a millstone on his head from a tower.
-
B.
Terbegec
Terbegec is a village that was part of the Kingdom of Hungary at the time of Ernő Gerő’s birth and is now located in modern-day Slovakia.
-
C.
Tozzer
Tozzer is a surname most notably associated with Alfred Marston Tozzer, an American anthropologist and archaeologist known for his pioneering work on Mayan civilization.
-
D.
Zardoz
Zardoz is a 1974 science fiction film directed by John Boorman, known for its surreal, dystopian vision and starring Sean Connery in one of his most unconventional roles.
-
E.
Jebe
Jebe was one of Genghis Khan’s most brilliant generals, renowned for his daring cavalry campaigns and key role in the early Mongol conquests across Central Asia and into Eastern Europe.
- 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_69bd4415eee08190bdce70276e56a5b4 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd706825188190b854dca5ca2f9db6 |
completed | March 20, 2026, 4:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81c5f8ec8190834c624bae17adff |
completed | March 21, 2026, 11:32 a.m. |
Created at: March 20, 2026, 1:30 p.m.