Triple
T7904561
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Gump – Sally Field |
E183538
|
entity |
| Predicate | teachesProtagonist |
P60701
|
FINISHED |
| Object | acceptance |
—
|
LITERAL 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: acceptance | Statement: [Mrs. Gump – Sally Field, teachesProtagonist, acceptance]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: teachesProtagonist Context triple: [Mrs. Gump – Sally Field, teachesProtagonist, acceptance]
-
A.
protagonistIs
Indicates that one entity serves as the main character or central figure in relation to another entity or narrative context.
-
B.
protagonistType
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
-
C.
mainProtagonist
Indicates that the subject is the central character or primary focus in the narrative of the related work.
-
D.
didacticCharacter
chosen
Indicates that one entity serves a teaching or instructional role toward another, conveying guidance, lessons, or moral instruction.
-
E.
hasProtagonist
Indicates that a work of narrative has a main character who serves as its central focus or driving agent.
- F. None of above.
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_69ca828d13088190b222be7aa9f9315c |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a4331cc8190b50301c78767a850 |
completed | March 31, 2026, 3:06 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:02 p.m.