Triple
T14940472
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clarissa Vaughan |
E372510
|
entity |
| Predicate | settingOfStoryline |
P89733
|
FINISHED |
| Object | New York City in the 1990s |
—
|
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: New York City in the 1990s | Statement: [Clarissa Vaughan, settingOfStoryline, New York City in the 1990s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: settingOfStoryline Context triple: [Clarissa Vaughan, settingOfStoryline, New York City in the 1990s]
-
A.
settingOfNarration
chosen
Indicates the place, time, or context in which a narrated event or story takes place.
-
B.
storyEngine
Indicates that one entity functions as a narrative-generating or plot-controlling mechanism for another entity or set of events.
-
C.
storyline
Indicates that one entity serves as the narrative plot or sequence of events associated with another entity.
-
D.
setting
Indicates the place, time, or context in which an event, action, or interaction occurs.
-
E.
storyFunction
Indicates that one entity serves a particular narrative role or function within the story structure of another entity.
- 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_69d85cc9da0c81908d583ca3f63a3908 |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded64a2f24819099b21566756668a2 |
completed | April 15, 2026, 12:05 a.m. |
| PD | Predicate disambiguation | batch_69de9a588c2c8190b1245a1c406f447c |
completed | April 14, 2026, 7:49 p.m. |
Created at: April 10, 2026, 2:38 a.m.