Triple
T2472634
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canon 28 |
E55011
|
entity |
| Predicate | textCharacter |
P39686
|
FINISHED |
| Object | disciplinary canon |
—
|
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: disciplinary canon | Statement: [Canon 28, textCharacter, disciplinary canon]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: textCharacter Context triple: [Canon 28, textCharacter, disciplinary canon]
-
A.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
B.
graphicCharactersCount
Indicates the number of printable (non-control) characters present in a given text or string.
-
C.
textType
Indicates the classification of a text according to its type, format, or genre.
-
D.
characterAddressed
Indicates that one character directs speech, communication, or attention specifically toward another character.
-
E.
character1
Indicates that the subject is identified as the first or primary character in a narrative or context.
- 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_69ab49e279e88190ab10d7248aea9d11 |
completed | March 6, 2026, 9:40 p.m. |
| NER | Named-entity recognition | batch_69abd1eb3be481908fa7c6b8f1c78209 |
completed | March 7, 2026, 7:21 a.m. |
| PD | Predicate disambiguation | batch_69abd0b5e3d481909a5cbc4a96edd24f |
completed | March 7, 2026, 7:16 a.m. |
| PDg | Predicate description generation | batch_69abd1e45380819094b3f32a278bd457 |
completed | March 7, 2026, 7:21 a.m. |
Created at: March 6, 2026, 9:45 p.m.