Triple
T22122374
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Whipped Cream & Other Delights |
E546702
|
entity |
| Predicate | containsTrack |
P3284
|
FINISHED |
| Object | Tangerine |
—
|
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: Tangerine | Statement: [Whipped Cream & Other Delights, containsTrack, Tangerine]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tangerine Context triple: [Whipped Cream & Other Delights, containsTrack, Tangerine]
-
A.
Tangerine
Tangerine is a popular Afro-pop song by Nigerian singer Yemi Alade, known for its upbeat rhythm and vibrant, danceable style.
-
B.
Tangerine
"Tangerine" is a funk-infused hip hop track by Big Boi featuring T.I., known for its playful, club-ready sound and appearance on Big Boi’s debut solo album.
-
C.
Tangerine
"Tangerine" is a song by the English indie rock band Glass Animals, known for its dreamy production and introspective lyrics.
-
D.
Tangerine
chosen
"Tangerine" is a jazz standard frequently interpreted by prominent jazz musicians and vocalists.
-
E.
Tangerine
Tangerine is a fictional British assassin and one half of the hitman duo "Lemon and Tangerine" in the action-comedy film *Bullet Train*.
- 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_69e11e38b3848190ac3a4fa97d56e65a |
completed | April 16, 2026, 5:36 p.m. |
| NER | Named-entity recognition | batch_69f1297f3fb48190b6aaca18b40c37ab |
completed | April 28, 2026, 9:41 p.m. |
Created at: April 16, 2026, 8:31 p.m.