Triple
T36742004
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Devil's Candy |
E907643
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Zooey Hellman
Zooey Hellman is a fictional character from the horror film "The Devil's Candy," likely connected to the movie’s central themes of demonic influence and family peril.
|
E2198151
|
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: Zooey Hellman | Statement: [The Devil's Candy, hasCharacter, Zooey Hellman]
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: Zooey Hellman Triple: [The Devil's Candy, hasCharacter, Zooey Hellman]
Generated description
Zooey Hellman is a fictional character from the horror film "The Devil's Candy," likely connected to the movie’s central themes of demonic influence and family peril.
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_69f76e76d10881909ec1679bc043108c |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69f7c9003dac8190a28baf6cafc93f3a |
completed | May 3, 2026, 10:15 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3c173187cc8190981810d3892aca3b |
completed | June 24, 2026, 5:43 p.m. |
| NEDg | Description generation | batch_6a3c189bc68c8190bdd7e56b3d49f056 |
completed | June 24, 2026, 5:49 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3c56fc3dec81908c85d734cc6dbee2 |
completed | June 24, 2026, 10:15 p.m. |
Created at: May 3, 2026, 4:12 p.m.