Triple
T5348997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Theme from New York, New York |
E124125
|
entity |
| Predicate | hasFamousLyric |
P18290
|
FINISHED |
| Object | Start spreadin' the news |
—
|
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: Start spreadin' the news | Statement: [Theme from New York, New York, hasFamousLyric, Start spreadin' the news]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasFamousLyric Context triple: [Theme from New York, New York, hasFamousLyric, Start spreadin' the news]
-
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.
hasSongAbout
Indicates that one entity has created, features, or is associated with a song whose subject or theme is about another entity.
-
C.
hasPoeticLyrics
Indicates that something (such as a song, text, or speech) contains lyrics or wording that are artistic, expressive, or characteristic of poetry.
-
D.
hasLyricCharacter
Indicates that a musical work or song includes a specific character or persona within its lyrics.
-
E.
hasExplicitLyrics
Indicates that the referenced content contains explicit language or themes, such as profanity, sexual content, or strong violence.
- 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_69bd464be27081908807b40b75c1bbae |
completed | March 20, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69bd860ea7088190ad7be14132927d17 |
completed | March 20, 2026, 5:38 p.m. |
| PD | Predicate disambiguation | batch_69bd845c6f108190832a8d14b356368a |
completed | March 20, 2026, 5:31 p.m. |
Created at: March 20, 2026, 2:01 p.m.