Triple
T29193814
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | I Am Charlotte Simmons |
E740066
|
entity |
| Predicate | award |
P107
|
FINISHED |
| Object |
Bad Sex in Fiction Award
The Bad Sex in Fiction Award is a British literary prize given annually to an author for the most awkward or poorly written sex scene in a novel.
|
E1851863
|
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: Bad Sex in Fiction Award | Statement: [I Am Charlotte Simmons, award, Bad Sex in Fiction Award]
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: Bad Sex in Fiction Award Triple: [I Am Charlotte Simmons, award, Bad Sex in Fiction Award]
Generated description
The Bad Sex in Fiction Award is a British literary prize given annually to an author for the most awkward or poorly written sex scene in a novel.
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_69f07cb8033c8190b8807e219a14333d |
completed | April 28, 2026, 9:24 a.m. |
| NER | Named-entity recognition | batch_69f663c0a698819090eeeb219822e78a |
completed | May 2, 2026, 8:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a255087bae48190af7e977a2056a0ce |
completed | June 7, 2026, 11:05 a.m. |
| NEDg | Description generation | batch_6a25512ab0188190812bb5180cade68c |
completed | June 7, 2026, 11:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a255229df208190b51dfa1a1d195785 |
completed | June 7, 2026, 11:12 a.m. |
Created at: April 28, 2026, 12:03 p.m.