Triple
T23969623
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Desamuduru |
E604193
|
entity |
| Predicate | basedOnCharacterOccupation |
P154073
|
FINISHED |
| Object | TV journalist |
—
|
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: TV journalist | Statement: [Desamuduru, basedOnCharacterOccupation, TV journalist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: basedOnCharacterOccupation Context triple: [Desamuduru, basedOnCharacterOccupation, TV journalist]
-
A.
followsCharacterOccupation
Indicates that one character’s occupation or job role comes after or succeeds another character’s occupation in a sequence or progression.
-
B.
basedOnCharacterBy
Indicates that one work, adaptation, or portrayal is derived from or inspired by a character created by another entity.
-
C.
followsCharacterProfession
Indicates that one character’s professional role or occupation comes after or is modeled on another character’s profession.
-
D.
characterBasedOn
Indicates that one character is modeled, inspired, or derived from another real or fictional entity.
-
E.
basedOnCharacterFromWork
Indicates that one entity is derived from, inspired by, or modeled after a character that appears in another creative work.
- F. None of above. chosen
Provenance (4 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_69e29543019c8190872462e593cc50b4 |
completed | April 17, 2026, 8:17 p.m. |
| NER | Named-entity recognition | batch_69f1d1db392c8190a1044b75b898243a |
completed | April 29, 2026, 9:39 a.m. |
| PD | Predicate disambiguation | batch_69f161578d54819084a8b35496299993 |
completed | April 29, 2026, 1:39 a.m. |
| PDg | Predicate description generation | batch_69f167dca3608190ace9d2eef56b2af6 |
completed | April 29, 2026, 2:07 a.m. |
Created at: April 17, 2026, 9:25 p.m.