Triple
T13761895
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Kerr |
E330630
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Kerr |
E93876
|
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: Kerr | Statement: [William Kerr, familyName, Kerr]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kerr Context triple: [William Kerr, familyName, Kerr]
-
A.
Kerr
chosen
Kerr is a surname of Scottish origin borne by numerous notable individuals across fields such as politics, academia, sports, and the arts.
-
B.
Ker
Ker is a variant form of the surname Kerr, which is of Scottish origin and historically associated with Border Reiver families.
-
C.
Ker
Ker is a figure from Greek mythology personifying violent death and doom, often depicted as a malevolent spirit associated with battlefield slaughter.
-
D.
Kaluza
Kaluza is a surname most notably associated with Theodor Kaluza, the German physicist who proposed a unifying five-dimensional theory of gravity and electromagnetism.
-
E.
Kaiten
Kaiten was a Japanese warship that took part in the late-19th-century Boshin War naval engagements, including the Battle of Hakodate.
- 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_69d81c573f288190aa2403d484fa3d49 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de02250f4881908d5193b3d5d25844 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a8606dbc8190b0f7c38583986141 |
completed | May 3, 2026, 7:56 p.m. |
Created at: April 9, 2026, 10:09 p.m.