Triple
T2714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lee de Forest |
E51
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
de Forest
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
|
E494
|
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: de Forest | Statement: [Lee de Forest, familyName, de Forest]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: de Forest Context triple: [Lee de Forest, familyName, de Forest]
-
A.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
-
B.
Lynn
Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
-
C.
Delano
Delano is the middle name of Franklin D. Roosevelt, the 32nd president of the United States.
-
D.
Cape Cod
Cape Cod is a hook-shaped peninsula in southeastern Massachusetts known for its sandy beaches, maritime villages, and popular summer tourism.
-
E.
The Town of Homes
The Town of Homes is a residentially focused nickname for Belmont, Massachusetts, reflecting its suburban character and emphasis on neighborhood living.
- 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: de Forest Triple: [Lee de Forest, familyName, de Forest]
Generated description
de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: de Forest Target entity description: de Forest is a surname most notably associated with Lee de Forest, an American inventor and early pioneer of radio and electronic communication.
-
A.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
-
B.
Lynn
Lynn is a coastal city in northeastern Massachusetts, known as one of the larger urban centers in the Greater Boston metropolitan area.
-
C.
Delano
Delano is the middle name of Franklin D. Roosevelt, the 32nd president of the United States.
-
D.
Cape Cod
Cape Cod is a hook-shaped peninsula in southeastern Massachusetts known for its sandy beaches, maritime villages, and popular summer tourism.
-
E.
The Town of Homes
The Town of Homes is a residentially focused nickname for Belmont, Massachusetts, reflecting its suburban character and emphasis on neighborhood living.
- 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_69a22e0d37588190897cf37a323013f5 |
completed | Feb. 27, 2026, 11:51 p.m. |
| NER | Named-entity recognition | batch_69a230c560548190a57df2421e233775 |
completed | Feb. 28, 2026, 12:03 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a23d860c2881909010e0310acaf10f |
completed | Feb. 28, 2026, 12:57 a.m. |
| NEDg | Description generation | batch_69a23ffe6b4081908f9d04b169a7bed8 |
completed | Feb. 28, 2026, 1:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69a2406bedd081909e8704cef5ab08dd |
completed | Feb. 28, 2026, 1:10 a.m. |
Created at: Feb. 27, 2026, 11:55 p.m.