Triple
T33301218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Niagara wine region |
E852587
|
entity |
| Predicate | hasSubregion |
P285
|
FINISHED |
| Object |
Short Hills Bench
Short Hills Bench is a notable sub-appellation within Ontario’s Niagara Peninsula known for its sloping vineyards and cool-climate wines, particularly high-quality Pinot Noir and Chardonnay.
|
E2046279
|
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: Short Hills Bench | Statement: [Niagara wine region, hasSubregion, Short Hills Bench]
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: Short Hills Bench Triple: [Niagara wine region, hasSubregion, Short Hills Bench]
Generated description
Short Hills Bench is a notable sub-appellation within Ontario’s Niagara Peninsula known for its sloping vineyards and cool-climate wines, particularly high-quality Pinot Noir and Chardonnay.
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_69f34966ed4c81908dc9dda82d8c7fe3 |
completed | April 30, 2026, 12:21 p.m. |
| NER | Named-entity recognition | batch_69f6dea842b481909f3cd6514929a7ea |
completed | May 3, 2026, 5:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a354321e2008190a1031b3c3af87058 |
completed | June 19, 2026, 1:24 p.m. |
| NEDg | Description generation | batch_6a3544112e5c81909b7f1aa7fc559640 |
completed | June 19, 2026, 1:28 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a3548ae60f48190801d64acb5762591 |
completed | June 19, 2026, 1:48 p.m. |
Created at: May 1, 2026, 1:33 a.m.