Triple
T25468536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Nightmare Stacks |
E638237
|
entity |
| Predicate | featuresCharacter |
P626
|
FINISHED |
| Object |
Cassie Brewer
Cassie Brewer is a central character in Charles Stross’s novel "The Nightmare Stacks," notable for her role in the book’s blend of urban fantasy, espionage, and cosmic horror.
|
E1730640
|
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: Cassie Brewer | Statement: [The Nightmare Stacks, featuresCharacter, Cassie Brewer]
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: Cassie Brewer Triple: [The Nightmare Stacks, featuresCharacter, Cassie Brewer]
Generated description
Cassie Brewer is a central character in Charles Stross’s novel "The Nightmare Stacks," notable for her role in the book’s blend of urban fantasy, espionage, and cosmic horror.
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_69e75db9b964819096802dcf502e577e |
completed | April 21, 2026, 11:21 a.m. |
| NER | Named-entity recognition | batch_69f5f74f0e008190b3aa57b31c6cbe3e |
completed | May 2, 2026, 1:08 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11c7e21b9081908bac29b77cfc6315 |
completed | May 23, 2026, 3:29 p.m. |
| NEDg | Description generation | batch_6a11c8d922608190b7b1d32a42e986d5 |
completed | May 23, 2026, 3:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a11c99a6eec81909171f7d03a056fc8 |
completed | May 23, 2026, 3:36 p.m. |
Created at: April 21, 2026, 2:21 p.m.