Triple
T21293385
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Midtown High School |
E524852
|
entity |
| Predicate | notableRoleInStory |
P112925
|
FINISHED |
| Object | setting of Peter Parker’s teenage life |
—
|
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: setting of Peter Parker’s teenage life | Statement: [Midtown High School, notableRoleInStory, setting of Peter Parker’s teenage life]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: notableRoleInStory Context triple: [Midtown High School, notableRoleInStory, setting of Peter Parker’s teenage life]
-
A.
notableWorkInPlot
Indicates that a work is prominently featured or plays a significant role within the plot of another work.
-
B.
notableCharacterType
Indicates that an entity is a notable or prominent example of a specified character type or role.
-
C.
notableWorkRole
Indicates that a person’s role or position is specifically associated with the creation, performance, or contribution to a notable work.
-
D.
notableWorkCharacter
Indicates that a character appears in, is associated with, or plays a role in a particular notable work.
-
E.
notableFeatureInStory
chosen
Indicates that a particular feature, element, or characteristic plays a significant or prominent role within a story.
- 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_69e0b517e6748190850d6f6ddf323d69 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e73856ba988190a8359efecea362cc |
completed | April 21, 2026, 8:41 a.m. |
| PD | Predicate disambiguation | batch_69e61612ab748190a72b8703b938abcb |
completed | April 20, 2026, 12:03 p.m. |
Created at: April 16, 2026, 4:04 p.m.