Triple
T15165421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laughing Matter |
E362326
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object | Thin Air |
E578030
|
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: Thin Air | Statement: [Laughing Matter, hasPart, Thin Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thin Air Context triple: [Laughing Matter, hasPart, 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.
In the Air
"In the Air" is a song by the English rock band VII, featured as one of the tracks on their album.
-
C.
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.
-
D.
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.
-
E.
Luft
Luft is a surname most notably associated with Sid Luft, the American film producer and third husband of entertainer Judy Garland.
- 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_69d85a087b7c81908baa94a53dac8d68 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0064c6244819085daf8e1eafdf3f2 |
completed | April 15, 2026, 9:42 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fec885d68c8190999529b69bc34fec |
completed | May 9, 2026, 5:39 a.m. |
Created at: April 10, 2026, 3:08 a.m.