Triple
T22177330
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Gippsland Lakes |
E548082
|
entity |
| Predicate | near |
P350
|
FINISHED |
| Object | Paynesville |
—
|
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: Paynesville | Statement: [Gippsland Lakes, near, Paynesville]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Paynesville Context triple: [Gippsland Lakes, near, Paynesville]
-
A.
Paynesville
chosen
Paynesville is a coastal town in eastern Victoria, Australia, known as a gateway to the Gippsland Lakes and a popular destination for boating and waterside recreation.
-
B.
Paynesville
Paynesville is a major city in Liberia, located near the capital Monrovia and known for its role as a key urban and sporting center in the country.
-
C.
Yatesville
Yatesville is a small town located in the U.S. state of Georgia.
-
D.
Yatesville
Yatesville is a small borough in Luzerne County, Pennsylvania, situated near the city of Pittston in the northeastern part of the state.
-
E.
Barberton
Barberton is a small industrial city in northeastern Ohio known historically for its manufacturing base and proximity to Akron.
- 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_69e11e3d53f88190a2b690e3f25bb062 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a6d03488190b29872ff3f436237 |
completed | April 28, 2026, 9:45 p.m. |
Created at: April 16, 2026, 8:34 p.m.