Triple
T3126074
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Roses |
E65298
|
entity |
| Predicate | hasNotableLyricQuality |
P9125
|
FINISHED |
| Object | autobiographical |
—
|
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: autobiographical | Statement: [Roses, hasNotableLyricQuality, autobiographical]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableLyricQuality Context triple: [Roses, hasNotableLyricQuality, autobiographical]
-
A.
hasNoLyrics
Indicates that the referenced musical work or audio track does not contain any sung or spoken lyrics.
-
B.
hasLyric
Indicates that one entity (typically a musical work or track) contains or is associated with the lyrics provided by another entity.
-
C.
hasUnofficialLyrics
Indicates that an entity is associated with lyrics that are not officially authorized or recognized by the original creator or rights holder.
-
D.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
-
E.
notableSongCharacteristic
chosen
Indicates that a song is distinguished by a particular notable feature or quality, such as style, structure, or performance trait.
- 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_69ad8580c72481909672d37acf647893 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada52e9f348190aa15b2c0d252719f |
completed | March 8, 2026, 4:34 p.m. |
| PD | Predicate disambiguation | batch_69ad9df62e548190b053e1478deed467 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:04 p.m.