Triple
T21985578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scott, Missouri |
E542951
|
entity |
| Predicate | hasName |
P744
|
FINISHED |
| Object | Scott |
—
|
NE NERFINISHED |
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: Scott | Statement: [Scott, Missouri, hasName, Scott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Context triple: [Scott, Missouri, hasName, Scott]
-
A.
Scott
Scott is a central fictional character in Don DeLillo’s novel "Mao II," around whom key themes of identity, terrorism, and the role of the writer in contemporary society revolve.
-
B.
Scott
Scott is a common English-language surname borne by numerous notable individuals across fields such as literature, politics, science, and entertainment.
-
C.
Scott
Scott is a masculine given name of English and Scottish origin, traditionally meaning "a person from Scotland" or "Gaelic speaker."
-
D.
Scott
Scott is a supporting character in the surreal, semi-autobiographical comedy series "Lady Dynamite," which follows comedian Maria Bamford’s life and mental health struggles.
-
E.
Scott
Scott is the central protagonist of the 1989 romantic fantasy-comedy film "Ghosts Can't Do It," around whom the story’s supernatural and romantic plot revolves.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide. chosen
Provenance (2 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_69e0c48136b081908831fa907cc02e18 |
completed | April 16, 2026, 11:14 a.m. |
| NER | Named-entity recognition | batch_69f12708cdcc81909511d9f81bd8f20e |
completed | April 28, 2026, 9:30 p.m. |
Created at: April 16, 2026, 8:04 p.m.