Triple
T13719451
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Scott Disick |
E328987
|
entity |
| Predicate | givenName |
P17
|
FINISHED |
| Object | Scott |
E408189
|
NE 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: Scott | Statement: [Scott Disick, givenName, Scott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Context triple: [Scott Disick, givenName, 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 the middle name of Francis Scott Key, the American lawyer and poet who wrote the lyrics to the United States national anthem, "The Star-Spangled Banner."
-
C.
Scott
chosen
Scott is a common English-language surname borne by numerous notable individuals across fields such as literature, politics, science, and entertainment.
-
D.
Scott
Scott is a well-known Kimberly-Clark brand that offers paper-based hygiene and cleaning products such as toilet tissue, paper towels, and napkins.
-
E.
Kay
Kay is a common diminutive or nickname for the given name Catherine.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d80770b9bc81909f70c8c317d53cff |
completed | April 9, 2026, 8:09 p.m. |
| NER | Named-entity recognition | batch_69dd439a121c81908cae964e7756274c |
completed | April 13, 2026, 7:27 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f79d5c4f848190993f829824eabbef |
completed | May 3, 2026, 7:09 p.m. |
Created at: April 9, 2026, 9:55 p.m.