Triple
T26282323
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Cleaning Lady |
E661030
|
entity |
| Predicate | protagonistFormerProfession |
P35945
|
FINISHED |
| Object | Cambodian doctor |
—
|
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: Cambodian doctor | Statement: [The Cleaning Lady, protagonistFormerProfession, Cambodian doctor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: protagonistFormerProfession Context triple: [The Cleaning Lady, protagonistFormerProfession, Cambodian doctor]
-
A.
otherProtagonistOccupation
Indicates that another main character in the narrative has a specific occupation or job role.
-
B.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
C.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
D.
protagonistBackground
Indicates that one entity serves as the background, history, or prior circumstances of the protagonist entity in a narrative or story.
-
E.
protagonistSocialStatus
Indicates the social standing or class position held by the story’s main character in relation to others in their society.
- 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_69ee812bbd448190be4d7478b057990a |
completed | April 26, 2026, 9:18 p.m. |
| NER | Named-entity recognition | batch_69f60e74cdf08190b753c3c10691a440 |
completed | May 2, 2026, 2:47 p.m. |
| PD | Predicate disambiguation | batch_69f5f7ff548c8190a23e98c5e66e0bc7 |
completed | May 2, 2026, 1:11 p.m. |
Created at: April 26, 2026, 10:01 p.m.