Triple
T15211370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brian Banks (film character) |
E363521
|
entity |
| Predicate | educationLevelInStory |
P21535
|
FINISHED |
| Object | high school student |
—
|
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: high school student | Statement: [Brian Banks (film character), educationLevelInStory, high school student]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: educationLevelInStory Context triple: [Brian Banks (film character), educationLevelInStory, high school student]
-
A.
educationStatus
Indicates the current or achieved level, stage, or condition of an entity’s formal education.
-
B.
educationLevelCharacteristic
chosen
Indicates that one entity specifies, describes, or constrains the education level associated with another entity.
-
C.
educatedAt
Indicates that an entity received education or formal training at a specified institution or place of learning.
-
D.
educationType
Indicates the specific category or level of education associated with an entity, such as formal, informal, primary, secondary, or higher education.
-
E.
educationLevelAtIssue
Indicates that the relationship concerns the specific level of education being questioned, disputed, or otherwise central to a particular issue or context.
- 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_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. |
Created at: April 10, 2026, 3:11 a.m.