Triple
T18352603
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Where Did You Sleep Last Night |
E439704
|
entity |
| Predicate | hasLyricalSetting |
P84624
|
FINISHED |
| Object | rural American South |
—
|
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: rural American South | Statement: [Where Did You Sleep Last Night, hasLyricalSetting, rural American South]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyricalSetting Context triple: [Where Did You Sleep Last Night, hasLyricalSetting, rural American South]
-
A.
hasSettingInLyrics
chosen
Indicates that the lyrics of a work explicitly describe or reference a particular setting or location.
-
B.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
-
C.
lyricSetting
Indicates that one entity serves as the text or lyrics that are set to music or otherwise musically realized by another entity.
-
D.
hasLyricalForm
Indicates that one entity (typically a musical or poetic work) possesses or is characterized by a particular lyrical structure or form.
-
E.
hasLyricalTheme
Indicates that one entity (typically a creative work) features or is characterized by a particular lyrical subject, topic, or theme.
- 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_69d8b918221c8190a9f7b563d64ac677 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e514f8f07c8190a1506d4a327369d0 |
completed | April 19, 2026, 5:46 p.m. |
| PD | Predicate disambiguation | batch_69e44fe91bc08190906518e1b120fcf0 |
completed | April 19, 2026, 3:45 a.m. |
Created at: April 10, 2026, 10:37 a.m.