Triple
T33559631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The First Meetings |
E859579
|
entity |
| Predicate | hasLyricalPersona |
P45598
|
FINISHED |
| Object | first-person narrator |
—
|
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: first-person narrator | Statement: [The First Meetings, hasLyricalPersona, first-person narrator]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyricalPersona Context triple: [The First Meetings, hasLyricalPersona, first-person narrator]
-
A.
hasRecurringLyricalPersona
Indicates that an artist or author repeatedly uses the same or a very similar character, voice, or narrative persona across multiple lyrical or poetic works.
-
B.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
-
C.
lyricalCharacter
chosen
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.
hasLyricalTheme
Indicates that one entity (typically a creative work) features or is characterized by a particular lyrical subject, topic, or theme.
-
E.
hasLyricalLanguage
Indicates that something (such as a text or expression) employs poetic, expressive, or highly figurative language.
- 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_69f3497b2b68819093207971b5e13dc8 |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f6f711c5508190b8d93ce933508f1a |
completed | May 3, 2026, 7:19 a.m. |
| PD | Predicate disambiguation | batch_69f6f6632dfc8190af85e258c8519207 |
completed | May 3, 2026, 7:16 a.m. |
Created at: May 1, 2026, 1:40 a.m.