Triple
T20360446
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dolabella |
E496760
|
entity |
| Predicate | allegiance |
P1201
|
FINISHED |
| Object | Caesar |
—
|
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: Caesar | Statement: [Dolabella, allegiance, Caesar]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Caesar Context triple: [Dolabella, allegiance, Caesar]
-
A.
Caesar
Caesar was the title given to junior co-emperors in the Roman Empire, particularly formalized as the subordinate rank within Diocletian’s Tetrarchic system.
-
B.
Caesar
Caesar is the intelligent, evolved chimpanzee who leads the apes in the modern Planet of the Apes film series.
-
C.
Caesar
Caesar is one of the two canine protagonists in Robert Burns’s poem “The Twa Dogs,” serving as the more privileged dog whose conversations explore social class and human nature.
-
D.
Caesar
Caesar is a key character in Colson Whitehead’s novel "The Underground Railroad," an enslaved man whose partnership with Cora drives their perilous escape from bondage.
-
E.
Caesar
chosen
Caesar is a central character in Shakespeare’s play "Julius Caesar," depicted as a powerful Roman leader whose assassination triggers political chaos and moral conflict.
- 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_69e0b4a3f7f48190b37f354574028ca6 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e678584294819093788ccf2fb8e588 |
completed | April 20, 2026, 7:02 p.m. |
Created at: April 16, 2026, 11:25 a.m.