Triple
T38107809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roger of Ware |
E951569
|
entity |
| Predicate | healthSymbolism |
P190741
|
FINISHED |
| Object | his ulcer symbolizes moral corruption |
—
|
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: his ulcer symbolizes moral corruption | Statement: [Roger of Ware, healthSymbolism, his ulcer symbolizes moral corruption]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: healthSymbolism Context triple: [Roger of Ware, healthSymbolism, his ulcer symbolizes moral corruption]
-
A.
shapeSymbolism
Indicates how a particular shape is associated with or conveys symbolic meaning within a given context.
-
B.
eyeSymbolism
Indicates the use of eyes or eye-related imagery to symbolically represent deeper meanings, concepts, or themes in a context.
-
C.
languageOfSymbolism
Indicates that one entity is the language in which the symbolic meaning or symbolism of another entity is expressed or encoded.
-
D.
emblemSymbolism
Indicates that one entity serves as an emblem whose design or features symbolically represent or convey meanings about another entity.
-
E.
treeSymbolism
Indicates the use of a tree as a symbolic representation of an idea, quality, or relationship between entities.
- F. None of above. chosen
Provenance (4 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_69f76f065ed08190bdfb1b6d817f5b39 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69fcce2cf9188190b3f65b362203a6a3 |
completed | May 7, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69fcccee6240819084680887731ff64b |
completed | May 7, 2026, 5:33 p.m. |
| PDg | Predicate description generation | batch_69fccdd2d84481909a7ce22407def9c7 |
completed | May 7, 2026, 5:37 p.m. |
Created at: May 3, 2026, 4:21 p.m.