Triple
T9378217
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chelsea Morning |
E225705
|
entity |
| Predicate | hasLyricalFeature |
P4921
|
FINISHED |
| Object | vivid imagery |
—
|
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: vivid imagery | Statement: [Chelsea Morning, hasLyricalFeature, vivid imagery]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyricalFeature Context triple: [Chelsea Morning, hasLyricalFeature, vivid imagery]
-
A.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
-
B.
hasLyricalForm
Indicates that one entity (typically a musical or poetic work) possesses or is characterized by a particular lyrical structure or form.
-
C.
hasLyricalTheme
chosen
Indicates that one entity (typically a creative work) features or is characterized by a particular lyrical subject, topic, or theme.
-
D.
hasLyricalRegister
Indicates that something (such as a text, utterance, or expression) is associated with a particular lyrical or stylistic register in language.
-
E.
hasLyric
Indicates that one entity (typically a musical work or track) contains or is associated with the lyrics provided by another entity.
- 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_69ca842d8ee88190aaaa639aa953185d |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd50ac8aec8190a386c525e9cab7b1 |
completed | April 1, 2026, 5:06 p.m. |
| PD | Predicate disambiguation | batch_69cc7a6c733881908aa3070830c2100b |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:44 p.m.