Triple
T10928650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Audrey Marie Anderson |
E258139
|
entity |
| Predicate | hasRole |
P161
|
FINISHED |
| Object | Kim Brown |
E805461
|
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: Kim Brown | Statement: [Audrey Marie Anderson, hasRole, Kim Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kim Brown Context triple: [Audrey Marie Anderson, hasRole, Kim Brown]
-
A.
Kim Brown
Kim Brown is the central protagonist of "The Unit," around whom the story’s events and character dynamics primarily revolve.
-
B.
Kim Brown
chosen
Kim Brown is an individual known primarily through a close personal association with Tiffy Gerhardt.
-
C.
Kim Brown
Kim Brown is a person known primarily as a relative of Bob Brown.
-
D.
Lindsay Dole
Lindsay Dole is a driven and morally conflicted defense attorney on the legal drama series "The Practice," known for her complex personal and professional relationships within the firm.
-
E.
Mary Lou Fulton
Mary Lou Fulton was an American philanthropist and education advocate whose major gifts significantly supported teacher education and higher learning initiatives.
- 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_69d6aa864ed88190818280ab6791d065 |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d7709eb0ec819093f7d3f99097bbe4 |
completed | April 9, 2026, 9:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e217475344819088b44b6efb2df1c8 |
completed | April 17, 2026, 11:19 a.m. |
Created at: April 8, 2026, 9:22 p.m.