Triple
T1208752
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christopher Nourse |
E25949
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Nourse |
E25949
|
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: Nourse | Statement: [Christopher Nourse, hasSurname, Nourse]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nourse Context triple: [Christopher Nourse, hasSurname, Nourse]
-
A.
Nourse
chosen
Nourse is a surname and variant spelling of "Nurse," historically associated with English-speaking families and occasionally used as a place or business name.
-
B.
Redfield
Redfield is a surname of English origin borne by various notable individuals across fields such as politics, science, and the arts.
-
C.
Colwell
Colwell is a surname most notably associated with Rita R. Colwell, an influential American microbiologist and former director of the U.S. National Science Foundation.
-
D.
Ehrlich
Ehrlich is a German-origin surname borne by numerous notable individuals across fields such as science, medicine, and the arts.
-
E.
Blaustein
Blaustein is a municipality in the Alb-Donau district of Baden-Württemberg in southern Germany, situated near the city of Ulm.
- 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_69a4942b30f08190a91c60573e16b5ef |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bde30ce08190ab60a181ad2d321d |
completed | March 1, 2026, 10:29 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac93b9f65481908824573c225dc638 |
completed | March 7, 2026, 9:08 p.m. |
Created at: March 1, 2026, 7:46 p.m.