Triple
T20228841
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Born to Die |
E495462
|
entity |
| Predicate | followedBy |
P78
|
FINISHED |
| Object | Paradise |
—
|
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: Paradise | Statement: [Born to Die, followedBy, Paradise]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paradise Context triple: [Born to Die, followedBy, Paradise]
-
A.
Paradise
Paradise is the eternal, blissful abode in the hereafter promised by Allah to the righteous in Islamic belief.
-
B.
Paradise
chosen
"Paradise" is a popular song composed by Nacio Herb Brown, known for its classic Tin Pan Alley style and enduring presence in early American popular music.
-
C.
Paradise
"Paradise" is a critically acclaimed novel by Nobel Prize–winning author Abdulrazak Gurnah that explores colonialism, displacement, and coming-of-age in early 20th-century East Africa.
-
D.
Paradise
Paradise is a photographic series by German artist Thomas Struth that features large-scale, detailed images of lush, unspoiled natural landscapes.
-
E.
Paradise
Paradise is a film distribution company known for handling the release of the movie "Two Women."
- 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_69da626cff80819097b530718a7c98b6 |
completed | April 11, 2026, 3:02 p.m. |
| NER | Named-entity recognition | batch_69e66fdb61b08190b850a9648ebfb720 |
completed | April 20, 2026, 6:26 p.m. |
Created at: April 11, 2026, 11:39 p.m.