Triple
T6000768
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | God Bless America |
E133588
|
entity |
| Predicate | hasLyricsLine |
P18290
|
FINISHED |
| Object | God bless America, land that I love |
—
|
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: God bless America, land that I love | Statement: [God Bless America, hasLyricsLine, God bless America, land that I love]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyricsLine Context triple: [God Bless America, hasLyricsLine, God bless America, land that I love]
-
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.
hasLyricCharacter
Indicates that a musical work or song includes a specific character or persona within its lyrics.
-
C.
hasOpeningLyric
Indicates that one entity serves as the opening lyric of another entity, typically a song or musical work.
-
D.
hasRhymeLine
Indicates that one line of text rhymes with another line.
-
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_69c00872444c8190bfaf1739dcec765c |
completed | March 22, 2026, 3:19 p.m. |
| NER | Named-entity recognition | batch_69c04ee7c0e08190a6e78969448b070a |
completed | March 22, 2026, 8:19 p.m. |
| PD | Predicate disambiguation | batch_69c049e152e88190979ab80cb9b50321 |
completed | March 22, 2026, 7:58 p.m. |
Created at: March 22, 2026, 4:05 p.m.