Triple
T21660520
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | River Don (Tyne and Wear) |
E534581
|
entity |
| Predicate | flowsThrough |
P225
|
FINISHED |
| Object | Jarrow |
—
|
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: Jarrow | Statement: [River Don (Tyne and Wear), flowsThrough, Jarrow]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jarrow Context triple: [River Don (Tyne and Wear), flowsThrough, Jarrow]
-
A.
Jarrow
chosen
Jarrow is a historic town in northeast England known for its early medieval monastery and association with the scholar Venerable Bede.
-
B.
Ferring
Ferring is a coastal village and civil parish in West Sussex, England, known for its quiet residential character and seaside setting near Worthing.
-
C.
Londolovit
Londolovit is a settlement on the Lihir Islands of Papua New Guinea, known primarily for its proximity to the large Lihir gold mining operations.
-
D.
Swanson
Swanson is the surname of Joe Swanson, a main supporting character and paraplegic police officer in the animated television series "Family Guy."
-
E.
Swanson
Swanson is a well-known American food brand recognized for its canned broths, stocks, and frozen meals.
- 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_69e0c467e1f48190af2650b19175abc4 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69ef6c07bcb88190a9864672c20325ff |
completed | April 27, 2026, 2 p.m. |
Created at: April 16, 2026, 6:36 p.m.