Triple
T26899997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Rideau-Rockcliffe Ward |
E678000
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Fairhaven
Fairhaven is a residential neighbourhood located within Ottawa, Ontario, known for its suburban character and proximity to the city's eastern green spaces and waterways.
|
E1762030
|
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: Fairhaven | Statement: [Rideau-Rockcliffe Ward, contains, Fairhaven]
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: Fairhaven Triple: [Rideau-Rockcliffe Ward, contains, Fairhaven]
Generated description
Fairhaven is a residential neighbourhood located within Ottawa, Ontario, known for its suburban character and proximity to the city's eastern green spaces and waterways.
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_69eee9befee48190a26f214faa867be7 |
completed | April 27, 2026, 4:44 a.m. |
| NER | Named-entity recognition | batch_69f61fae0fc48190a9099a1e3d705a90 |
completed | May 2, 2026, 4 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a125362ce088190a4ab16355b72f113 |
completed | May 24, 2026, 1:24 a.m. |
| NEDg | Description generation | batch_6a12545544f881909f0afd8459986559 |
completed | May 24, 2026, 1:28 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a125879112c8190959380eaef8ccf19 |
completed | May 24, 2026, 1:46 a.m. |
Created at: April 27, 2026, 5:50 a.m.