Triple
T24458498
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nineteen Minutes |
E616753
|
entity |
| Predicate | followedBy |
P78
|
FINISHED |
| Object |
Change of Heart
"Change of Heart" is a novel by Jodi Picoult that explores themes of redemption, faith, and justice through the story of a death row inmate who offers to donate his heart to the daughter of one of his victims.
|
E616756
|
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: Change of Heart | Statement: [Nineteen Minutes, followedBy, Change of Heart]
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: Change of Heart Triple: [Nineteen Minutes, followedBy, Change of Heart]
Generated description
"Change of Heart" is a novel by Jodi Picoult that explores themes of redemption, faith, and justice through the story of a death row inmate who offers to donate his heart to the daughter of one of his victims.
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_69e2d7ef9fe08190a0613908758b4e86 |
completed | April 18, 2026, 1:01 a.m. |
| NER | Named-entity recognition | batch_69f298c8d854819091f1d92eef02b1b1 |
completed | April 29, 2026, 11:48 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0fe38aa3a88190aaffda5dcb8d699c |
completed | May 22, 2026, 5:03 a.m. |
| NEDg | Description generation | batch_6a0fe4bfcea881909cf308d946a88c4c |
completed | May 22, 2026, 5:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0fe54cbdac8190b45d5570023762c4 |
completed | May 22, 2026, 5:10 a.m. |
Created at: April 18, 2026, 2:19 a.m.