Triple
T9692795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 天安门 |
E234574
|
entity |
| Predicate | 象征意义 |
P129
|
FINISHED |
| Object | 中华人民共和国象征 |
—
|
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: 中华人民共和国象征 | Statement: [天安门, 象征意义, 中华人民共和国象征]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 象征意义 Context triple: [天安门, 象征意义, 中华人民共和国象征]
-
A.
shapeSymbolism
Indicates how a particular shape is associated with or conveys symbolic meaning within a given context.
-
B.
symbolizes
chosen
Indicates that one entity stands for, represents, or is used as a sign for another entity, concept, or idea.
-
C.
emblemSymbolism
Indicates that one entity serves as an emblem whose design or features symbolically represent or convey meanings about another entity.
-
D.
associatedCharacterMeaning
Indicates that there is a relationship between a character and the meaning, interpretation, or concept that this character is intended to represent.
-
E.
loreSignificance
Indicates that something holds notable importance, relevance, or impact within a fictional world’s backstory, mythology, or overarching narrative.
- 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_69ca84cb580c8190a7e5f4b3bcdaf2a4 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9d0727908190897894151c0ee7c2 |
completed | April 1, 2026, 10:32 p.m. |
| PD | Predicate disambiguation | batch_69ccd5b840f081909f66bf0b66d17d9b |
completed | April 1, 2026, 8:22 a.m. |
Created at: March 30, 2026, 8:17 p.m.