Triple
T11495137
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marian Brook |
E272513
|
entity |
| Predicate | relative |
P37
|
FINISHED |
| Object | Ada Brook |
E929098
|
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: Ada Brook | Statement: [Marian Brook, relative, Ada Brook]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ada Brook Context triple: [Marian Brook, relative, Ada Brook]
-
A.
Ada Brook
chosen
Ada Brook is a central character in the period drama series "The Gilded Age," portrayed as a genteel, unmarried woman navigating New York high society alongside her more domineering sister.
-
B.
Ada Law
Ada Law is one of the children of English actor Jude Law.
-
C.
Anita Borg
Anita Borg was an influential computer scientist and advocate for women in technology, best known for founding the Institute for Women and Technology (now AnitaB.org) and co-founding the Grace Hopper Celebration of Women in Computing.
-
D.
Esther Dyson
Esther Dyson is a prominent technology investor, journalist, and philanthropist known for her early involvement in the digital economy and advocacy on issues such as health, space, and technology policy.
-
E.
Kathryn Bostic
Kathryn Bostic is an American composer and pianist known for her evocative film and theater scores, particularly in independent cinema and stage productions.
- 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_69d6aae1b09881909ce2ded3fa0c14fa |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d85de183f08190abfa36eaabc61dc6 |
completed | April 10, 2026, 2:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e624c3691081908f2e448aebab40aa |
completed | April 20, 2026, 1:06 p.m. |
Created at: April 8, 2026, 9:36 p.m.