Triple
T27152191
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fenghuang |
E682419
|
entity |
| Predicate | bodyPartSymbolism |
P107935
|
FINISHED |
| Object | back represents propriety |
—
|
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: back represents propriety | Statement: [Fenghuang, bodyPartSymbolism, back represents propriety]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bodyPartSymbolism Context triple: [Fenghuang, bodyPartSymbolism, back represents propriety]
-
A.
shapeSymbolism
Indicates how a particular shape is associated with or conveys symbolic meaning within a given context.
-
B.
symbolismIn
chosen
Indicates that one entity functions as a symbol or representation within the context, meaning, or interpretive framework of another entity.
-
C.
languageOfSymbolism
Indicates that one entity is the language in which the symbolic meaning or symbolism of another entity is expressed or encoded.
-
D.
fieldSymbolism
Indicates the symbolic meaning or thematic associations that a field (as a setting, area, or domain) conveys within a given context.
-
E.
symbolismFocus
Indicates that the primary emphasis of a work, element, or representation is on its symbolic meaning rather than its literal or functional aspects.
- 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_69eefaceb2a08190b9659b7f730629f5 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f625411c14819086492062e86ba8d5 |
completed | May 2, 2026, 4:24 p.m. |
| PD | Predicate disambiguation | batch_69f623a91b9c8190b2e2fdbc55cb89b6 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 9:14 a.m.