Triple
T22608434
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Persian Motifs |
E566626
|
entity |
| Predicate | hasRecurringLyricalPersona |
P148914
|
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: [Persian Motifs, hasRecurringLyricalPersona, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRecurringLyricalPersona Context triple: [Persian Motifs, hasRecurringLyricalPersona, true]
-
A.
hasLyricalTheme
Indicates that one entity (typically a creative work) features or is characterized by a particular lyrical subject, topic, or theme.
-
B.
recurringLyric
Indicates that a particular lyric or line reappears multiple times within a song or musical piece.
-
C.
lyricalCharacter
Indicates that one entity is the character or persona expressed or portrayed in the lyrics of the other entity (such as a song or poem).
-
D.
hasLyricalRegister
Indicates that something (such as a text, utterance, or expression) is associated with a particular lyrical or stylistic register in language.
-
E.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
- F. None of above. chosen
Provenance (4 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_69e245884860819081046ce07d5872c4 |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f167e86794819097e9c1ea83db52e6 |
completed | April 29, 2026, 2:07 a.m. |
| PD | Predicate disambiguation | batch_69ee627be4248190889a88764624e174 |
completed | April 26, 2026, 7:07 p.m. |
| PDg | Predicate description generation | batch_69ee8841e9cc81908d23b34215e3be71 |
completed | April 26, 2026, 9:48 p.m. |
Created at: April 17, 2026, 2:55 p.m.