Triple
T37126318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Truth or Dare (2018 film) |
E919400
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Jillian Jacobs
Jillian Jacobs is a screenwriter best known for co-writing the 2018 supernatural horror film "Truth or Dare."
|
E2213488
|
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: Jillian Jacobs | Statement: [Truth or Dare (2018 film), writer, Jillian Jacobs]
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: Jillian Jacobs Triple: [Truth or Dare (2018 film), writer, Jillian Jacobs]
Generated description
Jillian Jacobs is a screenwriter best known for co-writing the 2018 supernatural horror film "Truth or Dare."
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_69f76e9d13e48190a108f7fbf80ff375 |
completed | May 3, 2026, 3:49 p.m. |
| NER | Named-entity recognition | batch_69fb303afb0081908d66c23dbe3f8344 |
completed | May 6, 2026, 12:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3f6a1f7db08190b20030e6c0290e60 |
completed | June 27, 2026, 6:13 a.m. |
| NEDg | Description generation | batch_6a3f6b00a9788190b91e5ef4c2af8340 |
completed | June 27, 2026, 6:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3f6b8877dc8190869db81917018452 |
completed | June 27, 2026, 6:19 a.m. |
Created at: May 3, 2026, 4:15 p.m.