Triple
T4453412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Please Please Me |
E97666
|
entity |
| Predicate | sideOne |
P50690
|
FINISHED |
| Object | Misery |
E439794
|
NE 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: Misery | Statement: [Please Please Me, sideOne, Misery]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Misery Context triple: [Please Please Me, sideOne, Misery]
-
A.
Misery
Misery is the first major section of the Heidelberg Catechism, focusing on humanity’s sinfulness and need for redemption.
-
B.
Misery
chosen
"Misery" is a song by the Beatles, written primarily by John Lennon with contributions from Paul McCartney, and released on their 1963 debut album "Please Please Me."
-
C.
Misery
Misery is a psychological horror novel by Stephen King about a famous author held captive by his deranged “number one fan.”
-
D.
Carrie
"Carrie" is Stephen King's debut horror novel, centered on a bullied teenage girl with telekinetic powers who exacts a devastating revenge on her tormentors.
-
E.
Carrie
Carrie is the charming and enigmatic American woman who becomes the central love interest in the British romantic comedy film "Four Weddings and a Funeral."
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69b3454777808190b78aa9047ba1f018 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3564216b081908c41109100b36862 |
completed | March 13, 2026, 12:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b6281cef58819095182f8c89fe6e59 |
completed | March 15, 2026, 3:31 a.m. |
Created at: March 12, 2026, 11:33 p.m.