Triple
T2077516
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Matthias N. Forney |
E44958
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object |
Forney
Forney is a surname of German origin borne by various notable individuals, including engineers, politicians, and artists.
|
E230658
|
NE FINISHED |
How this triple was built (4 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: [Matthias N. Forney, hasFamilyName, Forney]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Forney Context triple: [Matthias N. Forney, hasFamilyName, Forney]
-
A.
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.
-
B.
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.
-
C.
Duncanville
Duncanville is a suburban city in the Dallas–Fort Worth metropolitan area of North Texas.
-
D.
Wimberley
Wimberley is a small, scenic town in central Texas known for its picturesque Hill Country landscapes, swimming holes, and artsy, tourist-friendly downtown.
-
E.
Conroe
Conroe is a city in southeastern Texas, United States, located north of Houston and known for its rapid growth and proximity to Lake Conroe.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Forney Triple: [Matthias N. Forney, hasFamilyName, Forney]
Generated description
Forney is a surname of German origin borne by various notable individuals, including engineers, politicians, and artists.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Forney Target entity description: Forney is a surname of German origin borne by various notable individuals, including engineers, politicians, and artists.
-
A.
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.
-
B.
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.
-
C.
Duncanville
Duncanville is a suburban city in the Dallas–Fort Worth metropolitan area of North Texas.
-
D.
Wimberley
Wimberley is a small, scenic town in central Texas known for its picturesque Hill Country landscapes, swimming holes, and artsy, tourist-friendly downtown.
-
E.
Conroe
Conroe is a city in southeastern Texas, United States, located north of Houston and known for its rapid growth and proximity to Lake Conroe.
- F. None of above. chosen
Provenance (5 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_69a88916c2b48190a5ca2e9b12cad3ed |
completed | March 4, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69abba2fa9c48190958826d5226544df |
completed | March 7, 2026, 5:39 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ae2734a0688190a43c9687af48d06d |
completed | March 9, 2026, 1:49 a.m. |
| NEDg | Description generation | batch_69ae28155ec8819097741dcfd3d81110 |
completed | March 9, 2026, 1:53 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ae2889503c8190bc72e786c656edf3 |
completed | March 9, 2026, 1:55 a.m. |
Created at: March 4, 2026, 7:41 p.m.