Triple
T10667822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Nathan Appleton |
E251402
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Appleton |
E270280
|
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: Appleton | Statement: [Nathan Appleton, familyName, Appleton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Appleton Context triple: [Nathan Appleton, familyName, Appleton]
-
A.
Appleton
chosen
Appleton is an English-origin surname borne by various notable individuals across fields such as literature, politics, and science.
-
B.
Appleton
Appleton is a mid-sized city in eastern Wisconsin known for its paper industry heritage, proximity to the Fox River, and role as a regional economic and cultural center.
-
C.
River Falls
River Falls is a small town located in Covington County, Alabama, known for its rural character and proximity to the Conecuh River.
-
D.
Hartland
Hartland is a small rural town in northwestern Connecticut known for its forests, reservoirs, and low population density.
-
E.
Hartland
Hartland is a suburban village in Waukesha County, Wisconsin, known for its residential communities and proximity to the Milwaukee metropolitan area.
- 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_69d6aa5b0d2881909584b20efc5877f0 |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6f860790c81909c2c1d3c489ec5b4 |
completed | April 9, 2026, 12:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d97a9ceea08190944354d127f2c73b |
completed | April 10, 2026, 10:33 p.m. |
Created at: April 8, 2026, 9:08 p.m.