Triple
T3170283
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | A Wonderful Guy |
E66313
|
entity |
| Predicate | settingOfSong |
P1957
|
FINISHED |
| Object | a tropical island in the South Pacific |
—
|
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 tropical island in the South Pacific | Statement: [A Wonderful Guy, settingOfSong, a tropical island in the South Pacific]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfSong Context triple: [A Wonderful Guy, settingOfSong, a tropical island in the South Pacific]
-
A.
lyricSetting
Indicates that one entity serves as the text or lyrics that are set to music or otherwise musically realized by another entity.
-
B.
setting
chosen
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
C.
notableSongCharacteristic
Indicates that a song is distinguished by a particular notable feature or quality, such as style, structure, or performance trait.
-
D.
effectOfSong
Indicates the influence or impact that a particular song has on something, such as a listener, mood, or situation.
-
E.
hasMusicalSettingsBy
Indicates that a work has been set to music or musically arranged by a specified creator or composer.
- 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_69ad8585d7988190af37365331093ccd |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69ada64917e08190bf0466d2c5e43790 |
completed | March 8, 2026, 4:39 p.m. |
| PD | Predicate disambiguation | batch_69ad9e0076b4819094628f1ad10b8f68 |
completed | March 8, 2026, 4:04 p.m. |
Created at: March 8, 2026, 3:06 p.m.