Triple
T1864496
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Almost Doesn't Count |
E34888
|
entity |
| Predicate | hasKeyLyric |
P18290
|
FINISHED |
| Object | Almost made you love me, almost made you cry |
—
|
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: Almost made you love me, almost made you cry | Statement: [Almost Doesn't Count, hasKeyLyric, Almost made you love me, almost made you cry]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasKeyLyric Context triple: [Almost Doesn't Count, hasKeyLyric, Almost made you love me, almost made you cry]
-
A.
hasLyric
chosen
Indicates that one entity (typically a musical work or track) contains or is associated with the lyrics provided by another entity.
-
B.
hasVariableLyrics
Indicates that the lyrics of a song or musical piece change between different performances, versions, or contexts.
-
C.
hasOpeningLyric
Indicates that one entity serves as the opening lyric of another entity, typically a song or musical work.
-
D.
hasLyricsTheme
Indicates that the lyrics of a work primarily concern or revolve around a specified theme or subject.
-
E.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
- 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_69a88600b2f88190bc09303e68ab517e |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69abb16c09e48190a345c95eab59fd87 |
completed | March 7, 2026, 5:02 a.m. |
| PD | Predicate disambiguation | batch_69abafe02c3c819093a4744b476106ca |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:34 p.m.