Triple
T12209650
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Brown |
E290921
|
entity |
| Predicate | livesWith |
P4704
|
FINISHED |
| Object | Mary Brown |
E971833
|
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: Mary Brown | Statement: [Mr. Brown, livesWith, Mary Brown]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mary Brown Context triple: [Mr. Brown, livesWith, Mary Brown]
-
A.
Mary Brown
Mary Brown is a writer known for her contributions to contemporary literature.
-
B.
Mary Brown
chosen
Mary Brown is the wife of Mr. Brown, known primarily in relation to him as his spouse.
-
C.
Mary Bowne
Mary Bowne was a colonial-era New Yorker known primarily as the daughter of Quaker pioneer and religious freedom advocate John Bowne.
-
D.
Mary Pratt
Mary Pratt was a prominent Canadian realist painter renowned for her luminous, intimate depictions of everyday domestic scenes.
-
E.
Mary Wilkes
Mary Wilkes is the daughter of the 18th-century English radical politician and journalist John Wilkes.
- 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_69d6ab65923081909acfc61b7a612233 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91c7ed4688190b0546b784e36b0ec |
completed | April 10, 2026, 3:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f62a8c69308190bffae7b38cc5620b |
completed | May 2, 2026, 4:47 p.m. |
Created at: April 8, 2026, 9:51 p.m.