Triple
T31233797
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Highway to Heaven |
E796357
|
entity |
| Predicate | coProtagonistFormerOccupation |
P35945
|
FINISHED |
| Object | police officer |
—
|
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: police officer | Statement: [Highway to Heaven, coProtagonistFormerOccupation, police officer]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: coProtagonistFormerOccupation Context triple: [Highway to Heaven, coProtagonistFormerOccupation, police officer]
-
A.
characterFormerOccupation
chosen
Indicates that a character previously held a specific occupation but no longer does.
-
B.
otherProtagonistOccupation
Indicates that another main character in the narrative has a specific occupation or job role.
-
C.
featuresProtagonistOccupation
Indicates that the work’s main character has a specified occupation or job role.
-
D.
hasCoProtagonistOccupation
Indicates that two or more co-protagonists share a specified occupation or professional role.
-
E.
economicRolePast
Indicates that an entity previously held a specific economic function, position, or role in the past.
- 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_69f224db69ac81909a370adad6a7ac7c |
completed | April 29, 2026, 3:33 p.m. |
| NER | Named-entity recognition | batch_69f69dfdda708190be290c7bec205445 |
completed | May 3, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69f69d1a37e081908d1d86b90ff502bd |
completed | May 3, 2026, 12:55 a.m. |
Created at: April 29, 2026, 9:10 p.m.