Triple
T8974682
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin |
E214356
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Martinsen |
E154783
|
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: Martinsen | Statement: [Martin, hasVariant, Martinsen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Martinsen Context triple: [Martin, hasVariant, Martinsen]
-
A.
Martinsen
chosen
Martinsen is a Scandinavian patronymic surname meaning "son of Martin," related to the Spanish surname Martinez.
-
B.
Sakshaug
Sakshaug is a village in the municipality of Inderøy in Trøndelag county, Norway, known for its historic church and rural setting.
-
C.
Morten
Morten is a masculine given name commonly used in Scandinavian countries, derived from the Latin name Martinus.
-
D.
Karlssen
Karlssen is a Scandinavian-origin surname, likely a patronymic variant related to the more common name Carlson.
-
E.
Rygge
Rygge is a municipality in southeastern Norway, historically known for its military air station and proximity to the town of Moss.
- 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_69ca839dbf608190a2f5990477115d29 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6783abe48190840e652fc2acf28f |
completed | April 1, 2026, 12:32 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc966f7d881908f4f80c2a0d820fe |
completed | April 3, 2026, 2:06 p.m. |
Created at: March 30, 2026, 7:02 p.m.