Triple
T10446310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laurentius |
E246296
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Lourens |
E174314
|
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: Lourens | Statement: [Laurentius, hasVariant, Lourens]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lourens Context triple: [Laurentius, hasVariant, Lourens]
-
A.
Marthinus
Marthinus is a masculine given name of Afrikaans and Dutch origin, historically borne by several notable South African figures.
-
B.
Marais Louw
Marais Louw is a South African rugby union player known for his performances as a flanker in domestic and international competitions.
-
C.
De Wit
De Wit is a Dutch surname commonly borne by individuals of Dutch origin and often associated with historical figures from the Netherlands.
-
D.
Wikus van de Merwe
Wikus van de Merwe is the bumbling South African bureaucrat who becomes the reluctant, transforming protagonist at the center of the sci-fi film "District 9."
-
E.
Lorens
chosen
Lorens is a character from Paulo Coelho’s novel "Brida," serving as one of the key figures in the protagonist’s spiritual and personal journey.
- 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_69d381c04fe08190957c26c526a3b05a |
completed | April 6, 2026, 9:49 a.m. |
| NER | Named-entity recognition | batch_69d4fdc0520c819098d2d53ee46a89ae |
completed | April 7, 2026, 12:51 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d87eeae8788190b63534fa4f942ead |
completed | April 10, 2026, 4:39 a.m. |
Created at: April 6, 2026, 12:16 p.m.