Triple
T21944974
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cheeni Kum |
E541910
|
entity |
| Predicate | leadCharacterAgeDifference |
P37051
|
FINISHED |
| Object | 30 years |
—
|
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: 30 years | Statement: [Cheeni Kum, leadCharacterAgeDifference, 30 years]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: leadCharacterAgeDifference Context triple: [Cheeni Kum, leadCharacterAgeDifference, 30 years]
-
A.
spouseAgeDifference
Indicates the age gap between two individuals who are spouses in a marital relationship.
-
B.
portrayedByCharacterAgeApprox
Indicates that an entity is portrayed by a character whose age is approximately a specified value or age range.
-
C.
protagonistAge
chosen
Indicates the age of the main character or central figure in a narrative or scenario.
-
D.
protagonistAgeRelativeToPrequel
Indicates how the protagonist’s age in the current work compares to their age in a preceding prequel story.
-
E.
ageInSeries
Indicates the age of an entity as it appears or is depicted within a specific series or installment of a work.
- 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_69e0c47e2e5c81909a7f74ce3de50911 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f1242688988190a7b8f033c49368de |
completed | April 28, 2026, 9:18 p.m. |
| PD | Predicate disambiguation | batch_69e6f5efc208819091ed2cf6841fa600 |
completed | April 21, 2026, 3:58 a.m. |
Created at: April 16, 2026, 7:56 p.m.