Triple
T13469971
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zhu Biao |
E311601
|
entity |
| Predicate | characterReputation |
P22459
|
FINISHED |
| Object | benevolent |
—
|
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: benevolent | Statement: [Zhu Biao, characterReputation, benevolent]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: characterReputation Context triple: [Zhu Biao, characterReputation, benevolent]
-
A.
refersToPersonReputation
Indicates that something is about, concerns, or makes reference to a particular person's reputation.
-
B.
denotesReputationOf
Indicates that one entity represents, expresses, or serves as an indicator of the reputation or perceived standing of another entity.
-
C.
hasQuirkyReputation
Indicates that an entity is regarded by others as having an unusual, eccentric, or unconventional character or style.
-
D.
characterAlignment
chosen
Indicates the moral or ethical stance a character holds, typically along axes such as good–evil and lawful–chaotic.
-
E.
pietyReputation
Indicates the degree to which an entity is regarded as devout, virtuous, or religiously upright based on its perceived behavior or character.
- 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_69d806a938b8819097ec43a2229fc7f9 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69dbaf21e46081908a00c9acf54f270f |
completed | April 12, 2026, 2:41 p.m. |
| PD | Predicate disambiguation | batch_69dbadfddefc81909ef7fde23b181b5c |
completed | April 12, 2026, 2:36 p.m. |
Created at: April 9, 2026, 9:42 p.m.