Triple
T30590736
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Changli Xiansheng |
E778645
|
entity |
| Predicate | bearerInfluenced |
P152787
|
FINISHED |
| Object | Song dynasty Confucian scholars |
—
|
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: Song dynasty Confucian scholars | Statement: [Changli Xiansheng, bearerInfluenced, Song dynasty Confucian scholars]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: bearerInfluenced Context triple: [Changli Xiansheng, bearerInfluenced, Song dynasty Confucian scholars]
-
A.
influenced
Indicates that one entity has affected, shaped, or altered another entity’s state, behavior, or characteristics.
-
B.
influencedIn
Indicates that one entity had an effect on or shaped another entity within a specific context, domain, or setting.
-
C.
incorporatesInfluence
chosen
Indicates that one entity integrates or absorbs the influence, ideas, or characteristics of another into itself.
-
D.
influenceOf
Indicates that one entity affects, shapes, or alters the state, behavior, or properties of another entity.
-
E.
brokeredUnderInfluenceOf
Indicates that an agreement, deal, or arrangement was brokered while one or more involved parties were under the influence of another party or factor that significantly affected their decision-making.
- 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_69f224a1570c8190a85d3ac330479a79 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f6fb19063c81909466b329655c8583 |
completed | May 3, 2026, 7:36 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
Created at: April 29, 2026, 8:24 p.m.