Triple
T15211369
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Banks (film character) |
E363521
|
entity |
| Predicate | ageAtTimeOfConvictionInStory |
P117138
|
FINISHED |
| Object | teenager |
—
|
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: teenager | Statement: [Brian Banks (film character), ageAtTimeOfConvictionInStory, teenager]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ageAtTimeOfConvictionInStory Context triple: [Brian Banks (film character), ageAtTimeOfConvictionInStory, teenager]
-
A.
ageAtTimeOfAccusation
Indicates the age a person was at the specific time when an accusation was made against them.
-
B.
ageAtArrest
Indicates the age a person was at the time they were arrested.
-
C.
dateOfConviction
Indicates the specific calendar date on which a person or entity was formally found guilty of an offense.
-
D.
convictedOf
Indicates that a person or entity has been found guilty of committing a specified offense or crime through a formal legal process.
-
E.
convictionYear
Indicates the calendar year in which an entity was formally convicted of an offense.
- 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_69d85a0b78bc8190b6e5ad51a2c4cfc5 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e0076ad4ec81908d36f541fca08d72 |
completed | April 15, 2026, 9:47 p.m. |
| PD | Predicate disambiguation | batch_69deb97ee9d881908711dbe12a55283c |
completed | April 14, 2026, 10:02 p.m. |
| PDg | Predicate description generation | batch_69dec72059c08190a34f513a00185b08 |
completed | April 14, 2026, 11 p.m. |
Created at: April 10, 2026, 3:11 a.m.