Triple
T8306247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Scott of Foscote |
E194470
|
entity |
| Predicate | familyName |
P18
|
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: [Lord Scott of Foscote, familyName, Scott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Scott Context triple: [Lord Scott of Foscote, familyName, Scott]
-
A.
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."
-
B.
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.
-
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.
Kay
Kay is a common diminutive or nickname for the given name Catherine.
-
E.
Blaine
Blaine is the laid-back, surfing-obsessed teenage protagonist of the 1993 comedy film "Airborne," known for his inline skating skills and culture clash after moving from California to Cincinnati.
- 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_69ca82e613e88190bf8139669bbd0d53 |
completed | March 30, 2026, 2:04 p.m. |
| NER | Named-entity recognition | batch_69cb7f293db08190912e5e8bb7e940cf |
completed | March 31, 2026, 8 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cd955100448190862fd52d660585fd |
completed | April 1, 2026, 9:59 p.m. |
Created at: March 30, 2026, 5:54 p.m.