Triple
T25507896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Duessa |
E639293
|
entity |
| Predicate | symbolizesForReaders |
P129
|
FINISHED |
| Object | danger of trusting appearances |
—
|
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: danger of trusting appearances | Statement: [Duessa, symbolizesForReaders, danger of trusting appearances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: symbolizesForReaders Context triple: [Duessa, symbolizesForReaders, danger of trusting appearances]
-
A.
symbolismIn
Indicates that one entity functions as a symbol or representation within the context, meaning, or interpretive framework of another entity.
-
B.
symbolizes
chosen
Indicates that one entity stands for, represents, or is used as a sign for another entity, concept, or idea.
-
C.
symbolismFocus
Indicates that the primary emphasis of a work, element, or representation is on its symbolic meaning rather than its literal or functional aspects.
-
D.
languageOfSymbolism
Indicates that one entity is the language in which the symbolic meaning or symbolism of another entity is expressed or encoded.
-
E.
symbolInBook
Indicates a relationship where a particular symbol appears or is used within a specific book.
- 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_69e75dbd09308190b6b5f0afdc12ec6d |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f80749f88190a5c2a70e7370003b |
completed | May 2, 2026, 1:11 p.m. |
| PD | Predicate disambiguation | batch_69f5afd5baac8190bb8ed576813c8591 |
completed | May 2, 2026, 8:03 a.m. |
Created at: April 21, 2026, 2:47 p.m.