Triple
T12311585
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dorris Bowdon |
E293490
|
entity |
| Predicate | appearedInFilm |
P795
|
FINISHED |
| Object | Jennie |
E944112
|
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: Jennie | Statement: [Dorris Bowdon, appearedInFilm, Jennie]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jennie Context triple: [Dorris Bowdon, appearedInFilm, Jennie]
-
A.
Jennie
Jennie is a feminine given name commonly used as a diminutive or variant of Jennifer.
-
B.
Jennie Dean
Jennie Dean was an influential African American educator and community leader in Virginia who founded the Manassas Industrial School for Colored Youth in the late 19th century.
-
C.
Jennie Appleton
chosen
Jennie Appleton is the mysterious young girl who serves as the central, time-transcending figure in the fantasy romance film "Portrait of Jennie."
-
D.
Jenifer
Jenifer is a given name, typically a variant spelling of Jennifer, used primarily for females in English-speaking countries.
-
E.
Jenna
Jenna is a common feminine given name, often used as a diminutive or variant of Jennifer.
- 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_69d6ab6a2b50819082f6aedd32ed608a |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d93f02c0508190b10c0627cdaaba76 |
completed | April 10, 2026, 6:18 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f61e84fa708190854afc6afd425fd7 |
completed | May 2, 2026, 3:55 p.m. |
Created at: April 8, 2026, 9:53 p.m.