Triple
T15311961
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mud |
E366058
|
entity |
| Predicate | editedBy |
P1954
|
FINISHED |
| Object | Julie Monroe |
E370702
|
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: Julie Monroe | Statement: [Mud, editedBy, Julie Monroe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Julie Monroe Context triple: [Mud, editedBy, Julie Monroe]
-
A.
Julie Monroe
chosen
Julie Monroe is a film editor known for her work on major motion pictures, including the financial drama "Wall Street: Money Never Sleeps."
-
B.
Maxine Albro
Maxine Albro was an American muralist and painter associated with the New Deal era, best known for her vibrant frescoes and contributions to public art in San Francisco.
-
C.
Cara Seymour
Cara Seymour is a British actress known for her character roles in film and television, including her prominent part in the period medical drama series "The Knick."
-
D.
Louise Glaum
Louise Glaum was a prominent American silent film actress of the 1910s and early 1920s, best known for her sophisticated "vamp" roles in melodramas.
-
E.
Jo Harlow
Jo Harlow is a technology executive best known for leading mobile device and smartphone businesses at companies such as Nokia and later Microsoft.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03cd2d5a88190aead748920f93d47 |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a007d960bd08190b8ac366273646865 |
completed | May 10, 2026, 12:44 p.m. |
Created at: April 10, 2026, 3:16 a.m.