Triple
T11429663
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Henry Kagi |
E270844
|
entity |
| Predicate | parent |
P120
|
FINISHED |
| Object |
Mary Kagi
Mary Kagi was the mother of abolitionist John Henry Kagi, who was a key lieutenant of John Brown during the lead-up to the American Civil War.
|
E924917
|
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: Mary Kagi | Statement: [John Henry Kagi, parent, Mary Kagi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Kagi Context triple: [John Henry Kagi, parent, Mary Kagi]
-
A.
Kim Brown
Kim Brown is an individual known primarily through a close personal association with Tiffy Gerhardt.
-
B.
Kim Brown
Kim Brown is the central protagonist of "The Unit," around whom the story’s events and character dynamics primarily revolve.
-
C.
Kim Brown
Kim Brown is a person known primarily as a relative of Bob Brown.
-
D.
Lacey Beaty
Lacey Beaty is an American politician who serves as the mayor of Beaverton, Oregon, and is known for being the city's first female mayor.
-
E.
Bob Ferguson
Bob Ferguson is an American lawyer and politician who serves as the Attorney General of Washington State, known for leading numerous high-profile legal challenges on consumer protection, civil rights, and federal policies.
- 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: Mary Kagi Triple: [John Henry Kagi, parent, Mary Kagi]
Generated description
Mary Kagi was the mother of abolitionist John Henry Kagi, who was a key lieutenant of John Brown during the lead-up to the American Civil War.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mary Kagi Target entity description: Mary Kagi was the mother of abolitionist John Henry Kagi, who was a key lieutenant of John Brown during the lead-up to the American Civil War.
-
A.
Kim Brown
Kim Brown is an individual known primarily through a close personal association with Tiffy Gerhardt.
-
B.
Kim Brown
Kim Brown is a person known primarily as a relative of Bob Brown.
-
C.
Kim Brown
Kim Brown is the central protagonist of "The Unit," around whom the story’s events and character dynamics primarily revolve.
-
D.
Lacey Beaty
Lacey Beaty is an American politician who serves as the mayor of Beaverton, Oregon, and is known for being the city's first female mayor.
-
E.
Bob Ferguson
Bob Ferguson is an American lawyer and politician who serves as the Attorney General of Washington State, known for leading numerous high-profile legal challenges on consumer protection, civil rights, and federal policies.
- 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_69d6aadeef688190874bcecd88b3dd9b |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d806c1bfb881909720c74fe0fa837f |
completed | April 9, 2026, 8:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e5b8d923688190bb4d61d57768e10e |
completed | April 20, 2026, 5:25 a.m. |
| NEDg | Description generation | batch_69e5c28e2dd481909b45a43b5825f393 |
completed | April 20, 2026, 6:07 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69e5c4722c348190a4c49edb1f6df240 |
completed | April 20, 2026, 6:15 a.m. |
Created at: April 8, 2026, 9:35 p.m.