Triple
T12358200
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kumar Patel |
E294664
|
entity |
| Predicate | laterOccupationInFiction |
P104743
|
FINISHED |
| Object | 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: doctor | Statement: [Kumar Patel, laterOccupationInFiction, doctor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: laterOccupationInFiction Context triple: [Kumar Patel, laterOccupationInFiction, doctor]
-
A.
fictionalOccupation
Indicates that one entity is the imaginary or narrative-based job, role, or profession attributed to another entity within a fictional context.
-
B.
notableCharacterOccupation
Indicates that a notable character is associated with a specific occupation or professional role.
-
C.
characterFormerOccupation
Indicates that a character previously held a specific occupation but no longer does.
-
D.
laterInheritedByFictionalCharacter
Indicates that something originally associated with one entity is subsequently inherited or taken over by a fictional character.
-
E.
representedOccupation
Indicates that one entity has served as an official or formal representative of another entity’s occupation or professional role.
- 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_69d6ab6d8a4081908636601e69ddf262 |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d942a2d6e08190a13c7ff89af09354 |
completed | April 10, 2026, 6:34 p.m. |
| PD | Predicate disambiguation | batch_69d93ecf6b548190a394b6b56a0c1c68 |
completed | April 10, 2026, 6:17 p.m. |
| PDg | Predicate description generation | batch_69d9429ff2bc8190b09adf8f57fad451 |
completed | April 10, 2026, 6:34 p.m. |
Created at: April 8, 2026, 9:54 p.m.