Triple
T671513
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Lumberjack Song |
E12980
|
entity |
| Predicate | hasLyric |
P18290
|
FINISHED |
| Object | "I sleep all night and I work all day" |
—
|
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: "I sleep all night and I work all day" | Statement: [The Lumberjack Song, hasLyric, "I sleep all night and I work all day"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyric Context triple: [The Lumberjack Song, hasLyric, "I sleep all night and I work all day"]
-
A.
hasLyricsTheme
Indicates that the lyrics of a work primarily concern or revolve around a specified theme or subject.
-
B.
hasOpeningLyric
Indicates that one entity serves as the opening lyric of another entity, typically a song or musical work.
-
C.
hasLyricsSource
Indicates that one entity serves as the source or origin of the lyrics associated with another entity.
-
D.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
-
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. 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_69a493355dec819098d4244b2fa34885 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a4a1b3682c8190a9b9a454480c3446 |
completed | March 1, 2026, 8:29 p.m. |
| PD | Predicate disambiguation | batch_69a49d1a16c48190af89e3b078a4957e |
completed | March 1, 2026, 8:10 p.m. |
| PDg | Predicate description generation | batch_69a4a1b240648190bf15415205399d97 |
completed | March 1, 2026, 8:29 p.m. |
Created at: March 1, 2026, 7:36 p.m.