Triple
T35265519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Too Much |
E1018500
|
entity |
| Predicate | containsAutobiographicalLyrics |
P19920
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Too Much, containsAutobiographicalLyrics, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: containsAutobiographicalLyrics Context triple: [Too Much, containsAutobiographicalLyrics, true]
-
A.
hasAutobiographicalElements
chosen
Indicates that something, such as a work or narrative, contains elements drawn from the creator’s own life or personal experiences.
-
B.
hasAutobiographicalSubject
Indicates that something (such as a work, text, or narrative) has a subject that is the author or creator’s own life or personal experiences.
-
C.
hasEnigmaticLyrics
Indicates that the lyrics associated with an entity are mysterious, obscure, or difficult to interpret.
-
D.
hasMemorableLyric
Indicates that something (such as a song, verse, or musical piece) contains a lyric that is especially striking, distinctive, or easy to remember.
-
E.
hasConfessionalLyrics
Indicates that the lyrics express deeply personal, intimate, or self-revealing thoughts, feelings, or experiences of the speaker.
- 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_69f76de4be5c8190a51705c07612cac8 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_6a00b8e0a5508190abc5c1e492bed12e |
completed | May 10, 2026, 4:57 p.m. |
| PD | Predicate disambiguation | batch_6a00b8327d048190850af317f60f0f8b |
completed | May 10, 2026, 4:54 p.m. |
Created at: May 3, 2026, 4:02 p.m.