Triple
T29285538
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Red Road |
E742498
|
entity |
| Predicate | hasCharacter |
P2308
|
FINISHED |
| Object |
Junior Van Der Veen
Junior Van Der Veen is a character in the television drama series "The Red Road," which centers on tensions between a small town and a nearby Native American community.
|
E1859366
|
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: Junior Van Der Veen | Statement: [The Red Road, hasCharacter, Junior Van Der Veen]
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: Junior Van Der Veen Triple: [The Red Road, hasCharacter, Junior Van Der Veen]
Generated description
Junior Van Der Veen is a character in the television drama series "The Red Road," which centers on tensions between a small town and a nearby Native American community.
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_69f09121ed8c8190b4cb27be3619c262 |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f66519d4b88190a035f0a51b69de2f |
completed | May 2, 2026, 8:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a2589432edc8190b123520f1c32bec0 |
completed | June 7, 2026, 3:07 p.m. |
| NEDg | Description generation | batch_6a258f23d22c8190bc376f4d03c0e00b |
completed | June 7, 2026, 3:32 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a259327460081909a0004657a10d2a7 |
completed | June 7, 2026, 3:49 p.m. |
Created at: April 28, 2026, 12:57 p.m.