Triple
T13451411
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Heavenly court |
E320614
|
entity |
| Predicate | hasMetaphoricalAspect |
P81126
|
FINISHED |
| Object | courtroom imagery |
—
|
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: courtroom imagery | Statement: [Heavenly court, hasMetaphoricalAspect, courtroom imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasMetaphoricalAspect Context triple: [Heavenly court, hasMetaphoricalAspect, courtroom imagery]
-
A.
hasMetaphoricalForm
chosen
Indicates that one entity is expressed, represented, or understood through a metaphorical form or figurative expression involving another entity.
-
B.
figurativeMeaning
Indicates that one entity is used in a non-literal, metaphorical, or symbolic sense to convey a meaning about another entity or concept.
-
C.
keyMetaphor
Indicates that one entity functions as a central or primary metaphor used to conceptualize, explain, or structure understanding of another entity.
-
D.
primaryMetaphor
Indicates a fundamental conceptual mapping where one domain (often concrete or physical) is systematically understood in terms of another (often abstract), forming a basic metaphorical relationship between them.
-
E.
hasLanguageAspect
Indicates that an entity is associated with a particular linguistic aspect, such as tense, mood, or grammatical feature, in relation to a language.
- 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_69d80761e6cc8190a90c844589998ecc |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dbaef973b08190a3d7fe1c2a913cff |
completed | April 12, 2026, 2:40 p.m. |
| PD | Predicate disambiguation | batch_69d9a03ce03481908c61094f0cc0c158 |
completed | April 11, 2026, 1:13 a.m. |
Created at: April 9, 2026, 9:41 p.m.