Triple
T19981933
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Volumnia |
E493835
|
entity |
| Predicate | literaryAnalysisTopic |
P36841
|
FINISHED |
| Object | mother–son relationship in tragedy |
—
|
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: mother–son relationship in tragedy | Statement: [Volumnia, literaryAnalysisTopic, mother–son relationship in tragedy]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: literaryAnalysisTopic Context triple: [Volumnia, literaryAnalysisTopic, mother–son relationship in tragedy]
-
A.
literarySubject
chosen
Indicates that one entity serves as the subject, topic, or focus of a literary work created by another entity.
-
B.
literaryCriticismType
Indicates the specific kind or category of literary criticism applied to a work, author, or text.
-
C.
literaryThemeInvolvement
Indicates the involvement or presence of a particular literary theme within a work, passage, or character arc.
-
D.
literaryInterest
Indicates that one entity has an interest in, appreciation of, or engagement with the literary works or writings of another entity.
-
E.
inLiterature
Indicates that a work, concept, or entity is mentioned, discussed, or represented within a piece of literature.
- 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_69da626a67648190af9653832a3aeced |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e65d13a8a88190bf5f4f697793f4c9 |
completed | April 20, 2026, 5:06 p.m. |
| PD | Predicate disambiguation | batch_69e537fae79c81909eae39500766d0b6 |
completed | April 19, 2026, 8:15 p.m. |
Created at: April 11, 2026, 3:28 p.m.