Triple
T7767911
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Mary’s College |
E178995
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Victoria |
E145661
|
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: Victoria | Statement: [St Mary’s College, locatedIn, Victoria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Victoria Context triple: [St Mary’s College, locatedIn, Victoria]
-
A.
Victoria
Victoria is a coastal municipality in the province of Northern Samar in the Philippines, known for its rural communities and agricultural economy.
-
B.
Victoria
Victoria is a vengeful vampire antagonist from the Twilight series who relentlessly hunts Bella Swan and opposes the Cullen family.
-
C.
Victoria
Victoria was the eldest child of Queen Victoria and Prince Albert, who became German Empress and Queen of Prussia through her marriage to Frederick III.
-
D.
Victoria
chosen
Victoria is a coastal city on the southern tip of Vancouver Island known for its historic architecture, mild climate, and vibrant tourism industry.
-
E.
Victoria
Victoria is a Canadian national honour recognizing outstanding achievement and service, associated with the motto of the Order of Canada (CVO).
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69c69f30602c819082ab52cd4af5c592 |
completed | March 27, 2026, 3:16 p.m. |
| NER | Named-entity recognition | batch_69c70435b7f88190a5e68e6ae701c58f |
completed | March 27, 2026, 10:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8c7c12e0081908eb984ba9bc558ff |
completed | March 29, 2026, 6:33 a.m. |
Created at: March 27, 2026, 4:11 p.m.