Triple
T13893519
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bedtime Stories |
E334029
|
entity |
| Predicate | notableSingle |
P3283
|
FINISHED |
| Object | Take a Bow |
E407131
|
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: Take a Bow | Statement: [Bedtime Stories, notableSingle, Take a Bow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Take a Bow Context triple: [Bedtime Stories, notableSingle, Take a Bow]
-
A.
Take a Bow
"Take a Bow" is a mid-1990s R&B ballad best known as a hit single by Madonna, notable for its smooth, melancholic tone and strong chart success worldwide.
-
B.
Take a Bow
chosen
"Take a Bow" is a mid-tempo pop and R&B ballad by Rihanna, known for its breakup theme and commercial success worldwide.
-
C.
Down to One
"Down to One" is a country song by Luke Bryan featured on his album "Born Here Live Here Die Here."
-
D.
And Still
"And Still" is a reflective, emotionally charged song by English musician Peter Gabriel, known for its atmospheric production and introspective lyrics.
-
E.
On Bended Knee
"On Bended Knee" is a hit R&B ballad by Boyz II Men, known for its emotional lyrics and powerful vocal harmonies.
- 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_69d81c5dd2d48190b7a5fc1e009de936 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de23a537d4819093c2bae2a244816a |
completed | April 14, 2026, 11:23 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7ce7419cc81909488871c16d6b356 |
completed | May 3, 2026, 10:38 p.m. |
Created at: April 9, 2026, 10:15 p.m.