Triple
T37816702
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Canadian raising |
E942794
|
entity |
| Predicate | hasOrthographicOpacity |
P199260
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Canadian raising, hasOrthographicOpacity, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasOrthographicOpacity Context triple: [Canadian raising, hasOrthographicOpacity, true]
-
A.
hasOrthographicSpace
Indicates that there is a space character or spacing separation between the written forms of the related entities in orthographic representation.
-
B.
hasOrthographicPreference
Indicates that one entity prefers or selects a particular written or spelling form of another entity.
-
C.
orthographicProperty
Indicates a relationship where a specific written or spelling-related characteristic is attributed to or associated with an entity.
-
D.
orthographicLength
Indicates the number of written characters or symbols used to represent an entity in a particular orthographic form.
-
E.
hasOrthographicVariantType
Indicates that one form of a written expression is classified as a specific type of orthographic variant of another (e.g., spelling, script, or character variation).
- 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_69f76ee987588190906506e759be5db3 |
completed | May 3, 2026, 3:51 p.m. |
| NER | Named-entity recognition | batch_69ff289541e0819096eeceb8e6332650 |
completed | May 9, 2026, 12:29 p.m. |
| PD | Predicate disambiguation | batch_69ff281ab1988190920f0443be9f10cc |
completed | May 9, 2026, 12:27 p.m. |
| PDg | Predicate description generation | batch_69ff28948b34819090e7e8b0b19535b2 |
completed | May 9, 2026, 12:29 p.m. |
Created at: May 3, 2026, 4:19 p.m.