Triple
T27642340
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reborn |
E696619
|
entity |
| Predicate | hasProtagonistOccupationInLife |
P21567
|
FINISHED |
| Object | retired architect |
—
|
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: retired architect | Statement: [Reborn, hasProtagonistOccupationInLife, retired architect]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasProtagonistOccupationInLife Context triple: [Reborn, hasProtagonistOccupationInLife, retired architect]
-
A.
hasOccupationDuringStory
Indicates that an entity holds or performs a particular occupation or job role during the time span covered by the story.
-
B.
otherProtagonistOccupation
Indicates that another main character in the narrative has a specific occupation or job role.
-
C.
protagonistParentOccupation
Indicates the occupation or job held by the protagonist’s parent in the described context.
-
D.
hasOccupationInReality
Indicates that an entity holds or performs a specific occupation in the real world, as opposed to fictional or hypothetical contexts.
-
E.
featuresProtagonistOccupation
chosen
Indicates that the work’s main character has a specified occupation or job role.
- 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_69ef5909f3848190805f35b76833e722 |
completed | April 27, 2026, 12:39 p.m. |
| NER | Named-entity recognition | batch_69fe163a41a0819098403b470e327d29 |
completed | May 8, 2026, 4:58 p.m. |
| PD | Predicate disambiguation | batch_69fe1358db5c819092570814a37ef5bd |
completed | May 8, 2026, 4:46 p.m. |
Created at: April 27, 2026, 2:27 p.m.