Triple
T31878651
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | High Plains Tango |
E813815
|
entity |
| Predicate | hasProseQuality |
P166576
|
FINISHED |
| Object | emotionally expressive |
—
|
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: emotionally expressive | Statement: [High Plains Tango, hasProseQuality, emotionally expressive]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProseQuality Context triple: [High Plains Tango, hasProseQuality, emotionally expressive]
-
A.
hasProseElements
Indicates that something contains or is composed of prose components, such as sentences, paragraphs, or narrative text.
-
B.
hasProseDialogue
Indicates that one entity contains or features spoken or conversational content expressed in prose form involving another entity.
-
C.
hasProseAndVerse
Indicates that something contains both prose and verse forms within it.
-
D.
qualityInFiction
Indicates that a particular quality, trait, or characteristic is exhibited by an entity within a fictional context or work.
-
E.
hasLiteraryTone
chosen
Indicates that something (such as a text, expression, or communication) possesses a style or manner of expression characteristic of literature or literary writing.
- 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_69f348ed74bc81909846aaa6a3c7318c |
completed | April 30, 2026, 12:19 p.m. |
| NER | Named-entity recognition | batch_6a0091ad8b8c8190b0f00a3358e59bc1 |
completed | May 10, 2026, 2:09 p.m. |
| PD | Predicate disambiguation | batch_6a008f2813ec81909a54c2dfa5c75dc7 |
completed | May 10, 2026, 1:59 p.m. |
Created at: April 30, 2026, 11:56 p.m.