Triple
T26935013
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Beverly D'Angelo as Patsy Cline |
E678348
|
entity |
| Predicate | inUniverseGenre |
P164301
|
FINISHED |
| Object | country |
—
|
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: country | Statement: [Beverly D'Angelo as Patsy Cline, inUniverseGenre, country]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: inUniverseGenre Context triple: [Beverly D'Angelo as Patsy Cline, inUniverseGenre, country]
-
A.
genreWithin
Indicates that one genre is a subgenre or more specific category contained within another, broader genre.
-
B.
hasFictionalUniverseGenre
Indicates that a fictional universe is associated with a particular genre that characterizes its overall style, themes, or narrative type.
-
C.
inUniverseCulture
Indicates that a cultural practice, norm, or tradition exists within and is characteristic of a particular fictional or defined universe.
-
D.
targetGenre
Indicates the genre that something is specifically aimed at, categorized under, or intended to belong to.
-
E.
coveredInGenre
Indicates that a work or item is associated with, categorized under, or treated within a particular genre.
- 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_69eeeb4cac908190a45956c2993d1cc2 |
completed | April 27, 2026, 4:51 a.m. |
| NER | Named-entity recognition | batch_69f644de4a84819087ddb84757fc4585 |
completed | May 2, 2026, 6:39 p.m. |
| PD | Predicate disambiguation | batch_69f641dc8ff48190ab575d855616580c |
completed | May 2, 2026, 6:26 p.m. |
| PDg | Predicate description generation | batch_69f643e818d481908fc66bc91bd25d77 |
completed | May 2, 2026, 6:35 p.m. |
Created at: April 27, 2026, 6:15 a.m.