Triple
T21069949
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Skunk |
E519076
|
entity |
| Predicate | imageryFocus |
P64074
|
FINISHED |
| Object | skunk’s movement |
—
|
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: skunk’s movement | Statement: [The Skunk, imageryFocus, skunk’s movement]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: imageryFocus Context triple: [The Skunk, imageryFocus, skunk’s movement]
-
A.
depictionFocus
Indicates that a depiction (such as an image or illustration) is primarily focused on or centered around a particular entity or subject.
-
B.
focusTheory
Indicates that one entity serves as the primary theoretical framework, perspective, or model that another entity is based on, organized around, or chiefly concerned with.
-
C.
usesImagery
chosen
Indicates that one entity employs descriptive or figurative language to create sensory or vivid mental images in relation to another entity or concept.
-
D.
focusesOn
Indicates that one entity directs its attention, effort, or primary activity toward another entity or specific subject.
-
E.
focusFeature
Indicates that one entity is the primary or emphasized feature, aspect, or attribute being highlighted or concentrated on in relation to another.
- 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_69e0b505ef108190b25dd4033e2ff7eb |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e6feb7b1d081909363c8bff785164b |
completed | April 21, 2026, 4:36 a.m. |
| PD | Predicate disambiguation | batch_69e5dbf9d71881908cd85dfc37db93ca |
completed | April 20, 2026, 7:55 a.m. |
Created at: April 16, 2026, 2:46 p.m.