Triple
T16011635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Richard Morgan |
E388348
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
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.
|
E1189409
|
NE FINISHED |
How this triple was built (4 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: [Richard Morgan, notableWork, Thin Air]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thin Air Context triple: [Richard Morgan, notableWork, Thin Air]
-
A.
Thin Air
"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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Thin Air Triple: [Richard Morgan, notableWork, Thin Air]
Generated description
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.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Thin Air Target entity description: 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.
-
A.
Thin Air
"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. chosen
Provenance (5 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_69d86dabcb7c8190b6a39d6831d2fa1b |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1829119648190aef5b5e84b26d898 |
completed | April 17, 2026, 12:45 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffcf267a9c81908f1fa1ad117c5e2c |
completed | May 10, 2026, 12:19 a.m. |
| NEDg | Description generation | batch_69ffd0bfc05881908d7223c52050ea14 |
completed | May 10, 2026, 12:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffd159dbcc81908ac586a6b8de57cf |
completed | May 10, 2026, 12:29 a.m. |
Created at: April 10, 2026, 4:55 a.m.