Triple
T30001399
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary in the Morning |
E762175
|
entity |
| Predicate | hasLyricsSubject |
P7609
|
FINISHED |
| Object | a man’s love for Mary |
—
|
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: a man’s love for Mary | Statement: [Mary in the Morning, hasLyricsSubject, a man’s love for Mary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyricsSubject Context triple: [Mary in the Morning, hasLyricsSubject, a man’s love for Mary]
-
A.
hasLyricsIn
Indicates that the lyrics of a work are written or available in a specified language.
-
B.
hasLyric
Indicates that one entity (typically a musical work or track) contains or is associated with the lyrics provided by another entity.
-
C.
hasLyricsTheme
chosen
Indicates that the lyrics of a work primarily concern or revolve around a specified theme or subject.
-
D.
hasLyricsMentioning
Indicates that the referenced lyrics explicitly mention or refer to the specified entity.
-
E.
hasLyricsSubjectAge
Indicates that the lyrics explicitly mention or describe the age of the subject.
- 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_69f2246a47ac81909cf5213053687ffc |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69f6794eecb48190a679439c69a17137 |
completed | May 2, 2026, 10:23 p.m. |
| PD | Predicate disambiguation | batch_69f66ec9919881908a187bfc7c4df192 |
completed | May 2, 2026, 9:38 p.m. |
Created at: April 29, 2026, 6:41 p.m.