Triple
T12663568
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Incident |
E302485
|
entity |
| Predicate | impactOnNarrator |
P102027
|
FINISHED |
| Object | dominates memory of Baltimore visit |
—
|
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: dominates memory of Baltimore visit | Statement: [Incident, impactOnNarrator, dominates memory of Baltimore visit]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: impactOnNarrator Context triple: [Incident, impactOnNarrator, dominates memory of Baltimore visit]
-
A.
narratorIn
Indicates that one entity serves as the narrator or storytelling voice within the context of another entity (such as a work, scene, or narrative segment).
-
B.
impactOnAuthor
Indicates that one entity has an effect, influence, or consequence on the author.
-
C.
impactOnSubject
chosen
Indicates the effect, influence, or consequence that one entity, event, or action has on a specified subject.
-
D.
impactOnDirector
Indicates that one entity has an effect, influence, or consequence on a director in the context of a given situation or action.
-
E.
impactDescription
Indicates a description of the effect, consequence, or influence that one entity, action, or event has on another.
- 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_69d7bded71a88190bb76e2413af9ea66 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9617c5b888190b37d4ede139bb49e |
completed | April 10, 2026, 8:45 p.m. |
| PD | Predicate disambiguation | batch_69d960b78ce8819091f15dd5013e6da5 |
completed | April 10, 2026, 8:42 p.m. |
Created at: April 9, 2026, 5:19 p.m.