Triple
T29299691
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mozhi |
E742925
|
entity |
| Predicate | protagonistPortrayedAs |
P20969
|
FINISHED |
| Object | deaf and mute woman |
—
|
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: deaf and mute woman | Statement: [Mozhi, protagonistPortrayedAs, deaf and mute woman]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistPortrayedAs Context triple: [Mozhi, protagonistPortrayedAs, deaf and mute woman]
-
A.
protagonistIs
Indicates that one entity serves as the main character or central figure in relation to another entity or narrative context.
-
B.
portrayedTitleCharacter
Indicates that one entity played the main or title role character associated with another entity (such as a work or production).
-
C.
protagonistType
chosen
Indicates the role or category that the main character (protagonist) of a story or scenario belongs to.
-
D.
protagonistBasedOn
Indicates that a fictional work’s main character is modeled on, inspired by, or derived from a particular real or fictional person or entity.
-
E.
characterPortrayedIs
Indicates that one entity serves as the fictional or dramatic role that is depicted or played by another entity.
- 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_69f09123ed9881909f351f7541933f5e |
completed | April 28, 2026, 10:51 a.m. |
| NER | Named-entity recognition | batch_69f68f670b608190a0b6ab60d722b4e0 |
completed | May 2, 2026, 11:57 p.m. |
| PD | Predicate disambiguation | batch_69f68b7b03488190b1db5fde4c7dd6e5 |
completed | May 2, 2026, 11:40 p.m. |
Created at: April 28, 2026, 1:09 p.m.