Triple
T16698071
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Man from the Alamo |
E405769
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Glenn Ford |
E250405
|
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: Glenn Ford | Statement: [The Man from the Alamo, starring, Glenn Ford]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glenn Ford Context triple: [The Man from the Alamo, starring, Glenn Ford]
-
A.
Glenn Ford
chosen
Glenn Ford was a Canadian-American film actor renowned for his versatile performances in classic Hollywood movies such as "Gilda," "The Big Heat," and "Blackboard Jungle."
-
B.
Richard Widmark
Richard Widmark was an American film and television actor renowned for his intense portrayals in film noir and Westerns, particularly during Hollywood’s mid-20th-century era.
-
C.
Ray Taylor
Ray Taylor was an American film director best known for his work on action-packed serials and B-movies during the early 20th century.
-
D.
Alan Ladd
Alan Ladd was an American film actor best known for his cool, understated performances in classic movies such as the Western "Shane."
-
E.
Dana Andrews
Dana Andrews was a prominent American film actor of the 1940s and 1950s, best known for his leading roles in classics such as "Laura" and "The Best Years of Our Lives."
- 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_69d8838db21081909589220fd71440a4 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e3832f550c8190bf7514d4611dec6a |
completed | April 18, 2026, 1:12 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a014136275c819084da2756632e0f48 |
completed | May 11, 2026, 2:38 a.m. |
Created at: April 10, 2026, 5:19 a.m.