Triple
T2902276
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beatniks |
E62679
|
entity |
| Predicate | stereotypedAppearance |
P311
|
FINISHED |
| Object | black turtlenecks |
—
|
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: black turtlenecks | Statement: [Beatniks, stereotypedAppearance, black turtlenecks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: stereotypedAppearance Context triple: [Beatniks, stereotypedAppearance, black turtlenecks]
-
A.
appearance
chosen
Indicates how something looks or seems to an observer, including its visible form, condition, or outward impression.
-
B.
styleTendsTo
Indicates that one style is generally inclined or likely to develop, appear, or be adopted in the direction of another style.
-
C.
typicalAppearanceContext
Indicates the usual situation, setting, or context in which an entity most commonly appears or is typically encountered.
-
D.
adaptationAppearance
Indicates that one entity appears or is depicted in an adaptation of another entity (such as a work being represented in a derived or reinterpreted version).
-
E.
genderStereotypingRecognizedAs
Indicates that a particular belief, behavior, or representation is acknowledged or classified as a form of gender stereotyping.
- 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_69ab4c3e070c8190b78d3d2c005876dd |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abe0b3d20881908cd4f1b465b504af |
completed | March 7, 2026, 8:24 a.m. |
| PD | Predicate disambiguation | batch_69abdd19bac881908f047d616aca8438 |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:10 p.m.