Triple
T29841178
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Affectionately Yours |
E757799
|
entity |
| Predicate | hasDennisMorganRole |
P194422
|
FINISHED |
| Object | Owen Wright |
—
|
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: Owen Wright | Statement: [Affectionately Yours, hasDennisMorganRole, Owen Wright]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasDennisMorganRole Context triple: [Affectionately Yours, hasDennisMorganRole, Owen Wright]
-
A.
hasTonyRobinsonRole
Indicates that an entity holds or is assigned a role associated with Tony Robinson.
-
B.
hasDrDreRole
Indicates that an entity holds or is assigned a specific role related to Dr. Dre (e.g., professional, creative, or collaborative capacity) in relation to another entity.
-
C.
hasFredMacMurrayRole
Indicates that an entity has a role or character portrayed by Fred MacMurray in a work (such as a film or television production).
-
D.
hasFranDrescherRole
Indicates that an entity plays, portrays, or holds a role associated with Fran Drescher in some context (such as a production, portrayal, or characterization).
-
E.
hasKhloeRole
Indicates that an entity holds or is assigned the specific role identified as "Khloe" in relation to another entity or context.
- 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_69f224593f6c81908785a560fe659f58 |
completed | April 29, 2026, 3:31 p.m. |
| NER | Named-entity recognition | batch_69fd6f9d600c8190acf495b7fc632e4b |
completed | May 8, 2026, 5:07 a.m. |
| PD | Predicate disambiguation | batch_69fd6e98a2948190a9f78c415ad23b8c |
completed | May 8, 2026, 5:03 a.m. |
| PDg | Predicate description generation | batch_69fd6f9a8bd881909983fe8f4cd0ba98 |
completed | May 8, 2026, 5:07 a.m. |
Created at: April 29, 2026, 5:39 p.m.