Triple
T35734531
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Four Beauties of ancient China |
E1032850
|
entity |
| Predicate | hasIdiom |
P183867
|
FINISHED |
| Object | 沉魚落雁 (fish sink, geese fall) |
—
|
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: 沉魚落雁 (fish sink, geese fall) | Statement: [Four Beauties of ancient China, hasIdiom, 沉魚落雁 (fish sink, geese fall)]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasIdiom Context triple: [Four Beauties of ancient China, hasIdiom, 沉魚落雁 (fish sink, geese fall)]
-
A.
hasSlang
Indicates that one entity is an informal, colloquial, or slang term referring to the other entity.
-
B.
pairedWithIdiomatically
Indicates that one entity is commonly or conventionally paired with another in idiomatic usage or expression.
-
C.
hasLatinPhraseMeaning
Indicates that one entity is a Latin phrase that expresses the meaning or translation of another entity.
-
D.
hasMeaningInJapanese
Indicates that something (such as a word, phrase, or symbol) possesses a specific meaning when interpreted in the Japanese language.
-
E.
hasCulturalEquivalent
Indicates that one entity has a counterpart in another cultural context that plays a similar role, function, or meaning.
- 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_69f76e10e59081908d81ad9ce22f40b6 |
completed | May 3, 2026, 3:47 p.m. |
| NER | Named-entity recognition | batch_69f7a34f8ee08190a040304635539a8f |
completed | May 3, 2026, 7:34 p.m. |
| PD | Predicate disambiguation | batch_69f7a06f125c8190843af194f042a465 |
completed | May 3, 2026, 7:22 p.m. |
| PDg | Predicate description generation | batch_69f7a34e80dc8190980d5b7b0b91341d |
completed | May 3, 2026, 7:34 p.m. |
Created at: May 3, 2026, 4:05 p.m.