Triple
T7505256
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Oak Lawn |
E177370
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Victoria |
E332516
|
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: [Oak Lawn, locatedIn, Victoria]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Victoria Context triple: [Oak Lawn, locatedIn, Victoria]
-
A.
Victoria
chosen
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 is a coastal city on the southern tip of Vancouver Island known for its historic architecture, mild climate, and vibrant tourism industry.
-
D.
Victoria
Victoria is a Canadian national honour recognizing outstanding achievement and service, associated with the motto of the Order of Canada (CVO).
-
E.
Victoria
Victoria is a British historical drama television series that chronicles the early life and reign of Queen Victoria.
- 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_69c69f2696688190915a8458f2398211 |
completed | March 27, 2026, 3:15 p.m. |
| NER | Named-entity recognition | batch_69c6f5b4c83c8190ace59f4d9e271904 |
completed | March 27, 2026, 9:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c83c9dcc2881908eea4efe075e894d |
completed | March 28, 2026, 8:39 p.m. |
Created at: March 27, 2026, 3:44 p.m.