Triple
T22654265
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stealing Fire |
E559181
|
entity |
| Predicate | hasPart |
P35
|
FINISHED |
| Object |
Dust and Diesel
Dust and Diesel is a component or segment of the larger work "Stealing Fire," likely representing one of its distinct parts or chapters.
|
E1547775
|
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: Dust and Diesel | Statement: [Stealing Fire, hasPart, Dust and Diesel]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dust and Diesel Context triple: [Stealing Fire, hasPart, Dust and Diesel]
-
A.
Dust
Dust is a crime novel by Patricia Cornwell featuring medical examiner Dr. Kay Scarpetta as she investigates a complex murder case linked to powerful institutions.
-
B.
Dust
Dust is a musical artist known for composing the score for the work "Learning To Die."
-
C.
Dust
Dust is a mysterious, conscious elementary particle central to the metaphysical and theological themes of Philip Pullman’s His Dark Materials universe.
-
D.
Dust
Dust is a Marvel Comics mutant superhero, often associated with the X-Men, who can transform her body into a swirling cloud of sand-like particles.
-
E.
Dust
Dust was an early 1970s American hard rock and proto–heavy metal band known for featuring future Ramones drummer Marky Ramone (then Marc Bell).
- 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: Dust and Diesel Triple: [Stealing Fire, hasPart, Dust and Diesel]
Generated description
Dust and Diesel is a component or segment of the larger work "Stealing Fire," likely representing one of its distinct parts or chapters.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Dust and Diesel Target entity description: Dust and Diesel is a component or segment of the larger work "Stealing Fire," likely representing one of its distinct parts or chapters.
-
A.
Dust
Dust is a crime novel by Patricia Cornwell featuring medical examiner Dr. Kay Scarpetta as she investigates a complex murder case linked to powerful institutions.
-
B.
Dust
Dust is a musical artist known for composing the score for the work "Learning To Die."
-
C.
Dust
Dust is a mysterious, conscious elementary particle central to the metaphysical and theological themes of Philip Pullman’s His Dark Materials universe.
-
D.
Dust
Dust is a Marvel Comics mutant superhero, often associated with the X-Men, who can transform her body into a swirling cloud of sand-like particles.
-
E.
Dust
Dust was an early 1970s American hard rock and proto–heavy metal band known for featuring future Ramones drummer Marky Ramone (then Marc Bell).
- 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_69e245489dd88190b1f674acf61c8769 |
completed | April 17, 2026, 2:35 p.m. |
| NER | Named-entity recognition | batch_69f1765a97ac819095f21ccbdada1d0a |
completed | April 29, 2026, 3:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0b58689074819081320f1e92e4bd8f |
completed | May 18, 2026, 6:20 p.m. |
| NEDg | Description generation | batch_6a0b6f5e95ec81909197b2dbca88d7df |
completed | May 18, 2026, 7:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0b7032e5f48190ada4ef53a71716a6 |
completed | May 18, 2026, 8:01 p.m. |
Created at: April 17, 2026, 3:06 p.m.