Triple
T6452912
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Virginia City |
E139914
|
entity |
| Predicate | MarkTwainRole |
P53328
|
FINISHED |
| Object | reporter for the Territorial Enterprise |
—
|
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: reporter for the Territorial Enterprise | Statement: [Virginia City, MarkTwainRole, reporter for the Territorial Enterprise]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: MarkTwainRole Context triple: [Virginia City, MarkTwainRole, reporter for the Territorial Enterprise]
-
A.
MarkTwainMoveInYear
Indicates the year in which Mark Twain moved to a particular place or residence.
-
B.
literaryRole
Indicates the specific narrative or functional role an entity holds within a literary work or text.
-
C.
speakerRole
chosen
Indicates the functional role or capacity in which an entity is acting as a speaker within a communicative event.
-
D.
narratorRole
Indicates that one entity serves as the narrator of another entity (such as a story, text, or media work), specifying the narrative role or function it performs.
-
E.
roleInStories
Indicates the specific function, position, or character part an entity plays within one or more stories.
- 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_69c008b301948190a35854e5284dc822 |
completed | March 22, 2026, 3:20 p.m. |
| NER | Named-entity recognition | batch_69c069d1c7c481909df9d2369edf5e74 |
completed | March 22, 2026, 10:14 p.m. |
| PD | Predicate disambiguation | batch_69c0673b44148190aed70084f0ff4992 |
completed | March 22, 2026, 10:03 p.m. |
Created at: March 22, 2026, 4:47 p.m.