Triple
T38567369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Manitoba Highway 10 |
E928261
|
entity |
| Predicate | passesThrough |
P225
|
FINISHED |
| Object |
Brandon
Brandon is a major city in southwestern Manitoba, Canada, known as an agricultural, educational, and commercial hub for the surrounding region.
|
E1459126
|
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: Brandon | Statement: [Manitoba Highway 10, passesThrough, Brandon]
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: Brandon Triple: [Manitoba Highway 10, passesThrough, Brandon]
Generated description
Brandon is a major city in southwestern Manitoba, Canada, known as an agricultural, educational, and commercial hub for the surrounding region.
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_69f76eb8d1808190a588af29d8b266d6 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_69fcd90b04b0819094913c584479f107 |
completed | May 7, 2026, 6:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a41e0478fb08190bf3c1ba5f59a56ae |
completed | June 29, 2026, 3:02 a.m. |
| NEDg | Description generation | batch_6a41e259cb5881908448f8159262a09f |
completed | June 29, 2026, 3:11 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a41e2bf2724819096eb210e4430f2ac |
completed | June 29, 2026, 3:13 a.m. |
Created at: May 3, 2026, 4:32 p.m.