Triple
T6850140
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kew |
E157994
|
entity |
| Predicate | state |
P87
|
FINISHED |
| Object | Victoria |
E20514
|
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: [Kew, state, Victoria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Victoria Context triple: [Kew, state, 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
chosen
Victoria is a southeastern Australian state known for its capital city Melbourne, cultural diversity, and varied landscapes ranging from coastal regions to alpine areas.
-
C.
Victoria
Victoria was the Spanish carrack that became the first ship to successfully circumnavigate the globe during Ferdinand Magellan’s expedition.
-
D.
Victoria
Victoria is a central London district known for its major transport hub, theatres, offices, and proximity to landmarks like Buckingham Palace.
-
E.
Victoria
Victoria is a vengeful vampire antagonist from the Twilight series who relentlessly hunts Bella Swan and opposes the Cullen family.
- 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_69c6882fae988190864cbba788c5ebb4 |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6d84c45708190918adfc028252400 |
completed | March 27, 2026, 7:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c72f901e848190a8240c23bccc4cbc |
completed | March 28, 2026, 1:32 a.m. |
Created at: March 27, 2026, 2:20 p.m.