Triple
T25909259
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Angus T. Jones |
E652843
|
entity |
| Predicate | participatedIn |
P149
|
FINISHED |
| Object |
Dinner with Friends
Dinner with Friends is a television or film production in which actor Angus T. Jones appeared.
|
E1702522
|
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: Dinner with Friends | Statement: [Angus T. Jones, participatedIn, Dinner with Friends]
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: Dinner with Friends Triple: [Angus T. Jones, participatedIn, Dinner with Friends]
Generated description
Dinner with Friends is a television or film production in which actor Angus T. Jones appeared.
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_69e7ab3d3f8481909bc53ed64c06af33 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f603c2ece48190812532cb235714ad |
completed | May 2, 2026, 2:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a10ecc9acb88190b00d90090301adbd |
completed | May 22, 2026, 11:54 p.m. |
| NEDg | Description generation | batch_6a10efdac5ac8190a3c71792453f5353 |
completed | May 23, 2026, 12:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a10f05d3850819082219e205528bcbb |
completed | May 23, 2026, 12:10 a.m. |
Created at: April 22, 2026, 8:28 a.m.