Triple
T9560533
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Forney |
E230658
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Forney |
E230658
|
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: Forney | Statement: [Jennifer Forney, familyName, Forney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Forney Context triple: [Jennifer Forney, familyName, Forney]
-
A.
Forney
chosen
Forney is a surname of German origin borne by various notable individuals, including engineers, politicians, and artists.
-
B.
Forney, Texas
Forney, Texas is a rapidly growing suburban city in the Dallas–Fort Worth metropolitan area known for its small-town feel and proximity to Dallas.
-
C.
Grand Prairie
Grand Prairie is a mid-sized suburban city in the Dallas–Fort Worth metropolitan area known for its family attractions, parks, and growing residential communities.
-
D.
Springtown
Springtown is a small rural community located within the township of Greater Madawaska in eastern Ontario, Canada.
-
E.
Duncanville
Duncanville is a suburban city in the Dallas–Fort Worth metropolitan area of North Texas.
- 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_69ca847e53a88190a60eed7e02257f10 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd994bde0c8190afcba5cb8fa8b984 |
completed | April 1, 2026, 10:16 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d1af4770f88190b9952c4308c0c384 |
completed | April 5, 2026, 12:39 a.m. |
Created at: March 30, 2026, 8:03 p.m.