Triple
T13914662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | 午门 |
E334588
|
entity |
| Predicate | 俗语影响 |
P112269
|
FINISHED |
| Object | “推出午门斩首”说法的来源之一 |
—
|
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: “推出午门斩首”说法的来源之一 | Statement: [午门, 俗语影响, “推出午门斩首”说法的来源之一]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: 俗语影响 Context triple: [午门, 俗语影响, “推出午门斩首”说法的来源之一]
-
A.
linguisticInfluence
Indicates that one entity has affected, shaped, or contributed to the language, style, or linguistic features of another entity.
-
B.
languageOfInfluence
Indicates a relationship where one language has influenced the development, usage, or characteristics of another language.
-
C.
hasCulturalImpact
Indicates that one entity has influenced, shaped, or significantly affected the culture, values, practices, or artistic expressions of another.
-
D.
influencedTradition
Indicates that one entity has had a shaping or guiding effect on the development, practices, or values of a particular tradition.
-
E.
influencesPractice
Indicates that one entity affects, shapes, or alters the way another entity carries out its activities, methods, or customary behavior.
- 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_69d81c5eaa9c819083b1ff8689179565 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de27245c648190b2946845ce0fdbf8 |
completed | April 14, 2026, 11:38 a.m. |
| PD | Predicate disambiguation | batch_69de059e4ba881908554f72e889719fa |
completed | April 14, 2026, 9:15 a.m. |
| PDg | Predicate description generation | batch_69de239524688190a0f2408c239cfcaa |
completed | April 14, 2026, 11:23 a.m. |
Created at: April 9, 2026, 10:16 p.m.