Triple
T23445108
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rumor and Sigh |
E565513
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | God Loves A Drunk |
—
|
NE NERFINISHED |
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: God Loves A Drunk | Statement: [Rumor and Sigh, hasTrack, God Loves A Drunk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: God Loves A Drunk Context triple: [Rumor and Sigh, hasTrack, God Loves A Drunk]
-
A.
God Loves a Drunk
chosen
"God Loves a Drunk" is a darkly humorous, folk-rock song by Richard Thompson that explores themes of faith, hypocrisy, and addiction through ironic, narrative lyrics.
-
B.
Be Drunk
"Be Drunk" is a prose poem by Charles Baudelaire urging readers to stay metaphorically intoxicated—on wine, poetry, or virtue—in order to escape the burdens of time and existence.
-
C.
Love You When I’m Drunk
"Love You When I’m Drunk" is a song by Mika featured on his album *The Origin of Love*.
-
D.
Drunk
"Drunk" is a song best known as a hit single by English singer-songwriter Ed Sheeran from his debut studio album, +.
-
E.
Drunk
Drunk is a 2010 Danish drama film directed by Thomas Vinterberg that explores the consequences of a group of teachers testing a theory that maintaining a constant level of alcohol in their blood will improve their lives.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e24584f9488190bb32730bd2ce023e |
completed | April 17, 2026, 2:36 p.m. |
| NER | Named-entity recognition | batch_69f1a647d6208190ba891252c8443fd4 |
completed | April 29, 2026, 6:33 a.m. |
Created at: April 17, 2026, 5:51 p.m.