Triple
T34373588
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mon oncle d’Amérique |
E882226
|
entity |
| Predicate | featuresTheoryOf |
P204104
|
FINISHED |
| Object | behaviorism |
—
|
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: behaviorism | Statement: [Mon oncle d’Amérique, featuresTheoryOf, behaviorism]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: featuresTheoryOf Context triple: [Mon oncle d’Amérique, featuresTheoryOf, behaviorism]
-
A.
featuresTopic
Indicates that something (such as a work, event, or item) prominently includes, focuses on, or is organized around a particular topic.
-
B.
featuresModel
Indicates that one entity includes, exposes, or is characterized by a particular model as one of its defining components or capabilities.
-
C.
featuresPractice
Indicates that something includes or offers a particular practice as a notable component or activity.
-
D.
featuresMethod
Indicates that an entity includes or provides a particular method as part of its functionality or behavior.
-
E.
featuredConcept
Indicates that one concept is highlighted or given special prominence relative to others in a particular 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_69f349bf5d7481908dd5da4cbdf74047 |
completed | April 30, 2026, 12:23 p.m. |
| NER | Named-entity recognition | batch_6a0324c2d618819093f7e6424b0f5baf |
completed | May 12, 2026, 1:01 p.m. |
| PD | Predicate disambiguation | batch_6a0324292e588190b37d0c3016ea2062 |
completed | May 12, 2026, 12:59 p.m. |
| PDg | Predicate description generation | batch_6a0324c1f89881909fc713e93e9c5f1c |
completed | May 12, 2026, 1:01 p.m. |
Created at: May 1, 2026, 1:59 a.m.