Triple
T27171594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I and I Survive |
E682926
|
entity |
| Predicate | hasLyricalMotive |
P86786
|
FINISHED |
| Object | use of phrase "I and I" |
—
|
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: use of phrase "I and I" | Statement: [I and I Survive, hasLyricalMotive, use of phrase "I and I"]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLyricalMotive Context triple: [I and I Survive, hasLyricalMotive, use of phrase "I and I"]
-
A.
hasLyricalTheme
Indicates that one entity (typically a creative work) features or is characterized by a particular lyrical subject, topic, or theme.
-
B.
lyricalMotive
chosen
Indicates a recurring musical or textual idea that serves as a unifying expressive element within a lyrical or vocal work.
-
C.
hasLyricalForm
Indicates that one entity (typically a musical or poetic work) possesses or is characterized by a particular lyrical structure or form.
-
D.
hasLyricalStyle
Indicates that one entity possesses or is characterized by a particular lyrical style in relation to another entity or context.
-
E.
usesLyricalElementsFrom
Indicates that one work incorporates or borrows lyrical components (such as words, phrases, or stylistic features) from another work.
- 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_69eefad086808190ab89816c0c300476 |
completed | April 27, 2026, 5:57 a.m. |
| NER | Named-entity recognition | batch_69f62547f92c8190818a05f9836a0843 |
completed | May 2, 2026, 4:24 p.m. |
| PD | Predicate disambiguation | batch_69f623a91b9c8190b2e2fdbc55cb89b6 |
completed | May 2, 2026, 4:17 p.m. |
Created at: April 27, 2026, 9:24 a.m.