Triple
T20700853
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Glenn Miller Orchestra |
E508776
|
entity |
| Predicate | notableSong |
P4
|
FINISHED |
| Object | In the Mood |
—
|
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: In the Mood | Statement: [Glenn Miller Orchestra, notableSong, In the Mood]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: In the Mood Context triple: [Glenn Miller Orchestra, notableSong, In the Mood]
-
A.
In the Mood
chosen
"In the Mood" is a famous big band-era jazz standard closely associated with Glenn Miller and widely recognized for its catchy swing rhythm and iconic saxophone riff.
-
B.
Get Her in the Mood
"Get Her in the Mood" is a track from the high-energy rock album "Adrenaline Rush" by Swedish singer Tove Lo.
-
C.
Dancin' Mood
"Dancin' Mood" is the B-side track to Rufus and Chaka Khan’s 1979 funk and R&B single "Do You Love What You Feel."
-
D.
I'm in the Mood
"I'm in the Mood" is a classic blues song by John Lee Hooker, renowned for its hypnotic groove and influential role in postwar electric blues.
-
E.
Get You in the Mood
"Get You in the Mood" is a song that appears as the B-side to the single "Take It Easy."
- 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_69e0b4c2b2a481909e31e9cb8f81ab55 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6c18bfac08190bf80beeb3ce1951a |
completed | April 21, 2026, 12:15 a.m. |
Created at: April 16, 2026, 12:12 p.m.