Triple
T32250795
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Convicted |
E823873
|
entity |
| Predicate | hasGlennFordRole |
P173883
|
FINISHED |
| Object | Joe Hufford |
—
|
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: Joe Hufford | Statement: [Convicted, hasGlennFordRole, Joe Hufford]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGlennFordRole Context triple: [Convicted, hasGlennFordRole, Joe Hufford]
-
A.
hasGingerRogersRole
Indicates that an entity is assigned or associated with a role specifically identified as the "Ginger Rogers" role in a given context or production.
-
B.
hasJoanFontaineRole
Indicates that an entity has a role played by Joan Fontaine in a film, television, or theatrical production.
-
C.
hasMarleneDietrichRoleType
Indicates that an entity has a specific type or category of role associated with Marlene Dietrich.
-
D.
hasElizabethTaylorRole
Indicates that an entity has a role that was originally played by, associated with, or famously portrayed by Elizabeth Taylor.
-
E.
hasHumanCast
Indicates that a work or production features human performers as part of its cast.
- F. None of above. chosen
Provenance (4 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_69f3490cdda88190a9d61e11252a771f |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_69f6bc36e36c8190a8cbabe17fb627d4 |
completed | May 3, 2026, 3:08 a.m. |
| PD | Predicate disambiguation | batch_69f6b632cf788190a3d0c08cd026b84b |
completed | May 3, 2026, 2:42 a.m. |
| PDg | Predicate description generation | batch_69f6b960ca4081909a77690c2b122f5e |
completed | May 3, 2026, 2:56 a.m. |
Created at: May 1, 2026, 12:40 a.m.