Triple
T9999032
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bob Brown |
E197275
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object | Serena Brown |
E833893
|
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: Serena Brown | Statement: [Bob Brown, hasChild, Serena Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serena Brown Context triple: [Bob Brown, hasChild, Serena Brown]
-
A.
Serena Brown
chosen
Serena Brown is the daughter of Bob Brown.
-
B.
Serena Evans
Serena Evans is a British actress best known for her role in the BBC sitcom "The Thin Blue Line."
-
C.
Serena Southerlyn
Serena Southerlyn is a fictional Assistant District Attorney on the long-running television series "Law & Order," known for her idealism and strong moral convictions in prosecuting cases.
-
D.
Serena Powers
Serena Powers is a relatively obscure individual whose specific public achievements or background are not widely documented.
-
E.
Serena Joy Waterford
Serena Joy Waterford is the strict, embittered Wife of a high-ranking Commander in Margaret Atwood’s "The Handmaid’s Tale," known for her complicity in and enforcement of Gilead’s oppressive regime.
- 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_69ca82f3b61c81908ecc2c1c96dbc2e4 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cdcc8c78448190a5332f4ff8a7b3dd |
completed | April 2, 2026, 1:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d26a36aadc81909978b71bdb3a6654 |
completed | April 5, 2026, 1:57 p.m. |
Created at: March 30, 2026, 8:51 p.m.