Triple
T20416997
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Alamo (2004 film) |
E500738
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Leslie Bohem |
—
|
NE NERFINISHED |
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: Leslie Bohem | Statement: [The Alamo (2004 film), screenwriter, Leslie Bohem]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leslie Bohem Context triple: [The Alamo (2004 film), screenwriter, Leslie Bohem]
-
A.
Leslie Bohem
chosen
Leslie Bohem is an American screenwriter and producer known for his work on science fiction and thriller films and television projects.
-
B.
Leslie Marr
Leslie Marr was a British landscape painter and amateur racing driver known for competing in Formula One in the 1950s while pursuing a successful artistic career.
-
C.
Connee Boswell
Connee Boswell was an American jazz and popular music singer, best known as a member of the Boswell Sisters and for her influential solo recordings in the 1930s and 1940s.
-
D.
Lila Leslie
Lila Leslie was a silent film actress active in the early 20th century, known for her roles in American dramas and melodramas.
-
E.
Leslie Morgan
Leslie Morgan is a distinguished academic recognized for her professorial role at Gresham College in London.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4a935588190b9446a99b37ced44 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e67a4437448190b07b6e6e3de5830f |
completed | April 20, 2026, 7:11 p.m. |
Created at: April 16, 2026, 11:30 a.m.