Triple
T20566408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nguyen Vietnam |
E504975
|
entity |
| Predicate | regardsAnalectsAs |
P140588
|
FINISHED |
| Object | canonical Confucian scripture |
—
|
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: canonical Confucian scripture | Statement: [Nguyen Vietnam, regardsAnalectsAs, canonical Confucian scripture]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regardsAnalectsAs Context triple: [Nguyen Vietnam, regardsAnalectsAs, canonical Confucian scripture]
-
A.
alsoRefersTo
Indicates that one term, label, or identifier is used as an alternative designation for the same entity or concept as another.
-
B.
hasAnaphora
Indicates that one expression in a text refers back to another earlier expression for its interpretation.
-
C.
notablyRefersTo
Indicates that one entity makes a particularly significant or noteworthy reference to another entity, beyond a routine or incidental mention.
-
D.
usesAnaphora
Indicates that one entity refers back to another previously mentioned entity using anaphoric expression (e.g., pronouns or repeated phrases).
-
E.
annexASubject
Indicates that one entity formally incorporates another entity as a subordinate or dependent part under its control.
- 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_69e0b4b6587c8190aee63dc7cff244ea |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6a7a228948190b47a3a61f239e00d |
completed | April 20, 2026, 10:24 p.m. |
| PD | Predicate disambiguation | batch_69e59ff0116c8190a163ff28ed01430a |
completed | April 20, 2026, 3:39 a.m. |
| PDg | Predicate description generation | batch_69e5a6a824748190bbe6192d73f3c613 |
completed | April 20, 2026, 4:08 a.m. |
Created at: April 16, 2026, 11:39 a.m.