Triple
T22708727
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anchor Drops |
E561532
|
entity |
| Predicate | hasTrack |
P3284
|
FINISHED |
| Object | Thin Air |
—
|
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: Thin Air | Statement: [Anchor Drops, hasTrack, Thin Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thin Air Context triple: [Anchor Drops, hasTrack, Thin Air]
-
A.
Thin Air
chosen
"Thin Air" is a track by the electronic music duo Binaural, known for its atmospheric, immersive sound design.
-
B.
Thin Air
Thin Air is a science fiction novel by Richard Morgan that blends noir detective elements with a gritty, near-future setting on a colonized Mars.
-
C.
In the Air
"In the Air" is a song by the English rock band VII, featured as one of the tracks on their album.
-
D.
In the Air
"In the Air" is a dreamy, atmospheric song by the American indie pop duo Beach House, known for its lush synths and ethereal vocals.
-
E.
The Air Up There
The Air Up There is a 1994 sports comedy film in which a college basketball coach travels to Africa to recruit a talented local player, blending fish-out-of-water humor with cross-cultural themes.
- 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_69e2454f1348819088d83f420925a5c1 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f178d11d4081909981872698b6c45e |
completed | April 29, 2026, 3:19 a.m. |
Created at: April 17, 2026, 3:17 p.m.