Triple
T2766310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Madison |
E61346
|
entity |
| Predicate | derivedFrom |
P909
|
FINISHED |
| Object |
Madison (surname)
Madison is an English-language surname that historically originated as a patronymic meaning “son of Maud” or “son of Matthew.”
|
E298065
|
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: Madison (surname) | Statement: [Madison, derivedFrom, Madison (surname)]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Madison (surname) Context triple: [Madison, derivedFrom, Madison (surname)]
-
A.
Mason
Mason is a common English surname of occupational origin, historically referring to a stoneworker or builder.
-
B.
Mason
Mason is the given first name of Red Cashion, the famed American NFL referee known for his exuberant "First down!" calls.
-
C.
Mason
Mason was an early 20th-century American automobile marque produced under the Durant Motors company, known for manufacturing mid-priced cars during the 1920s.
-
D.
Addison
Addison is a small, business-focused town in the Dallas–Fort Worth metropolitan area known for its dense concentration of restaurants, corporate offices, and frequent special events.
-
E.
Hayes
Hayes is a suburban district in southeast London, England, known for its residential character and green spaces within the London Borough of Bromley.
- 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: Madison (surname) Triple: [Madison, derivedFrom, Madison (surname)]
Generated description
Madison is an English-language surname that historically originated as a patronymic meaning “son of Maud” or “son of Matthew.”
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Madison (surname) Target entity description: Madison is an English-language surname that historically originated as a patronymic meaning “son of Maud” or “son of Matthew.”
-
A.
Mason
Mason is the given first name of Red Cashion, the famed American NFL referee known for his exuberant "First down!" calls.
-
B.
Mason
Mason is a common English surname of occupational origin, historically referring to a stoneworker or builder.
-
C.
Mason
Mason was an early 20th-century American automobile marque produced under the Durant Motors company, known for manufacturing mid-priced cars during the 1920s.
-
D.
Addison
Addison is a small, business-focused town in the Dallas–Fort Worth metropolitan area known for its dense concentration of restaurants, corporate offices, and frequent special events.
-
E.
Hayes
Hayes is a suburban district in southeast London, England, known for its residential character and green spaces within the London Borough of Bromley.
- 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_69ab4b7bab6c8190a5c2efef19a8ef34 |
completed | March 6, 2026, 9:47 p.m. |
| NER | Named-entity recognition | batch_69abdd5762d08190a6286994a4e5dd92 |
completed | March 7, 2026, 8:09 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69afc048bc8481908a6f70e034167c2a |
completed | March 10, 2026, 6:55 a.m. |
| NEDg | Description generation | batch_69afc14239e48190ad20f660e88befcb |
completed | March 10, 2026, 6:59 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69afc202466c81908c300520173837dc |
completed | March 10, 2026, 7:02 a.m. |
Created at: March 6, 2026, 9:57 p.m.