Triple
T28662728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Casca |
E725508
|
entity |
| Predicate | characterTypeInShakespeare |
P60013
|
FINISHED |
| Object | conspirator against Caesar |
—
|
LITERAL 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: conspirator against Caesar | Statement: [Casca, characterTypeInShakespeare, conspirator against Caesar]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterTypeInShakespeare Context triple: [Casca, characterTypeInShakespeare, conspirator against Caesar]
-
A.
roleInFamousPlay
Indicates that an entity portrays or has portrayed a specific character in a well-known theatrical play.
-
B.
characterIn
Indicates that an entity appears as a character within a specified work, story, or narrative.
-
C.
scriptureCharacterType
Indicates the type or role a character has within a scriptural or religious text (e.g., prophet, deity, disciple).
-
D.
typeOfCharacter
chosen
Indicates that one entity is a specific kind or category of character in relation to another entity.
-
E.
character3
Indicates a tertiary or additional character role associated with an entity, typically the third distinct character linked within a given context or work.
- F. None of above.
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_69f01d84f5f0819087ab5e6143b14ed7 |
completed | April 28, 2026, 2:37 a.m. |
| NER | Named-entity recognition | batch_69fcdf2394748190b35cead3e208447d |
completed | May 7, 2026, 6:51 p.m. |
| PD | Predicate disambiguation | batch_69fcdbe344ec8190a0471911952f4b82 |
completed | May 7, 2026, 6:37 p.m. |
Created at: April 28, 2026, 4:59 a.m.