Triple
T32353970
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leeds Ring Road |
E826682
|
entity |
| Predicate | hasJunctionWith |
P1018
|
FINISHED |
| Object |
A64 Leeds–York road
The A64 Leeds–York road is a major primary route in northern England connecting the cities of Leeds and York and continuing toward the Yorkshire coast.
|
E2003176
|
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: A64 Leeds–York road | Statement: [Leeds Ring Road, hasJunctionWith, A64 Leeds–York road]
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: A64 Leeds–York road Triple: [Leeds Ring Road, hasJunctionWith, A64 Leeds–York road]
Generated description
The A64 Leeds–York road is a major primary route in northern England connecting the cities of Leeds and York and continuing toward the Yorkshire coast.
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_69f34915a2588190bb3178f5ec2f48f4 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6be5bdfac81908a62443bb9ec78df |
completed | May 3, 2026, 3:17 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a33e8a2c4b8819080695604e87e6a2c |
completed | June 18, 2026, 12:46 p.m. |
| NEDg | Description generation | batch_6a33e9d65234819095512614ea0a0500 |
completed | June 18, 2026, 12:51 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a34192262b08190ae166d453ea6baf4 |
completed | June 18, 2026, 4:13 p.m. |
Created at: May 1, 2026, 12:49 a.m.