Triple
T32385465
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clerical Script |
E827533
|
entity |
| Predicate | strokeCharacteristic |
P47443
|
FINISHED |
| Object | rectilinear strokes |
—
|
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: rectilinear strokes | Statement: [Clerical Script, strokeCharacteristic, rectilinear strokes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: strokeCharacteristic Context triple: [Clerical Script, strokeCharacteristic, rectilinear strokes]
-
A.
spanCharacteristic
Indicates that one entity has a particular measurable or descriptive property that characterizes the extent, duration, or range of another entity or phenomenon.
-
B.
signatureFeature
Indicates that one entity is a defining or characteristic feature that distinctly identifies or typifies another entity.
-
C.
characterStyle
Indicates how a character is visually or typographically presented, such as its font, weight, size, or decorative attributes.
-
D.
usesLineCharacteristic
chosen
Indicates that one entity employs or is based on a specific characteristic or property of a line.
-
E.
calligraphicUse
Indicates that one entity uses or applies another entity specifically for calligraphic purposes or in the practice of calligraphy.
- 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_69f349184e7481909c6c54428cb9cf12 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6c1cf2aa081909f4c0b8f0cad1907 |
completed | May 3, 2026, 3:32 a.m. |
| PD | Predicate disambiguation | batch_69f6ba6eb32c8190bf405b2011fa48f7 |
completed | May 3, 2026, 3:01 a.m. |
Created at: May 1, 2026, 12:51 a.m.