Triple
T14958946
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenka Peterson |
E373007
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Peterson |
E325102
|
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: Peterson | Statement: [Lenka Peterson, familyName, Peterson]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Peterson Context triple: [Lenka Peterson, familyName, Peterson]
-
A.
Peterson
chosen
Peterson is a common English and Scandinavian patronymic surname meaning "son of Peter."
-
B.
Petersen
Petersen is a German surname borne by numerous notable individuals across fields such as film, sports, and politics.
-
C.
Peters
Peters is the married surname of Svetlana Iosifovna Stalina, the daughter of Soviet leader Joseph Stalin.
-
D.
Peters
Peters is a surname of English and German origin borne by numerous notable individuals across various fields.
-
E.
Peters
Peters is the family name of classic Hollywood film star Carole Lombard, known for her acclaimed roles in 1930s screwball comedies.
- 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_69d85cca979481908747d2a81eba1cea |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69ded6cd85bc81909040b7ff78f62554 |
completed | April 15, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe7ea1d78c81909b877fda05ef9231 |
completed | May 9, 2026, 12:24 a.m. |
Created at: April 10, 2026, 2:40 a.m.