Triple
T30968797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elegies (Propertius) |
E789032
|
entity |
| Predicate | characterTypeOfCynthia |
P60013
|
FINISHED |
| Object | elegiac mistress |
—
|
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: elegiac mistress | Statement: [Elegies (Propertius), characterTypeOfCynthia, elegiac mistress]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterTypeOfCynthia Context triple: [Elegies (Propertius), characterTypeOfCynthia, elegiac mistress]
-
A.
typeOfCharacter
chosen
Indicates that one entity is a specific kind or category of character in relation to another entity.
-
B.
characterTypeOfPrince
Indicates that the subject has the character type or role of a prince.
-
C.
characterPersona
Indicates that one entity embodies, represents, or assumes the persona, role, or character identity specified by another entity.
-
D.
helpsCharacterType
Indicates that one character type provides assistance or support to another character type.
-
E.
voiceTypeOfCharacter
Indicates the type or style of voice associated with a particular character.
- 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_69f224c3a6b48190951add9b7b7f0271 |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69fe1fd637c08190aa95cd2478c278cb |
completed | May 8, 2026, 5:39 p.m. |
| PD | Predicate disambiguation | batch_69fe19344bb481909b5e2144155e4add |
completed | May 8, 2026, 5:11 p.m. |
Created at: April 29, 2026, 8:54 p.m.