Triple
T17612867
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Gurney |
E429004
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Norfolk |
—
|
NE NERFINISHED |
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: Norfolk | Statement: [Elizabeth Gurney, placeOfBirth, Norfolk]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norfolk Context triple: [Elizabeth Gurney, placeOfBirth, Norfolk]
-
A.
Norfolk
chosen
Norfolk is a county in the East of England known for its rural landscapes, historic market towns, and extensive coastline along the North Sea.
-
B.
Norfolk
Norfolk is an independent coastal city in southeastern Virginia known for its major naval base and historic waterfront.
-
C.
Portsmouth
Portsmouth is a historic seaport city in southeastern New Hampshire known for its colonial-era architecture, maritime heritage, and vibrant cultural scene.
-
D.
Portsmouth
Portsmouth is a historic naval port city on England’s south coast, heavily targeted and damaged during the World War II Blitz.
-
E.
Portsmouth
Portsmouth is a residential neighborhood in North Portland, Oregon, known for its parks, schools, and diverse community.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d889e1c6148190ba76241e74688f8b |
completed | April 10, 2026, 5:25 a.m. |
| NER | Named-entity recognition | batch_69e46d2eaa348190a8226eef8c0d6e31 |
completed | April 19, 2026, 5:50 a.m. |
Created at: April 10, 2026, 5:51 a.m.