Triple
T12648713
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ruan Louw |
E302098
|
entity |
| Predicate | hasSurname |
P18
|
FINISHED |
| Object | Louw |
E52513
|
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: Louw | Statement: [Ruan Louw, hasSurname, Louw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Louw Context triple: [Ruan Louw, hasSurname, Louw]
-
A.
Louw
chosen
Louw is a surname of Afrikaans and Dutch origin borne by several notable South African figures, including writers and public personalities.
-
B.
Lourens
Lourens is a given name derived from the Latin name Laurentius, commonly used in Dutch and Afrikaans contexts.
-
C.
Marais Louw
Marais Louw is a South African rugby union player known for his performances as a flanker in domestic and international competitions.
-
D.
De Wit
De Wit is a Dutch surname commonly borne by individuals of Dutch origin and often associated with historical figures from the Netherlands.
-
E.
Leibbrandt
Leibbrandt is a German-language surname borne by various notable individuals across fields such as politics, sports, and academia.
- 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_69d7bdec9f9c8190b4bac675b7588211 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d9615cf6f48190bd0983cf7465ab15 |
completed | April 10, 2026, 8:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6687bbb408190b80da2d0310f82c5 |
completed | May 2, 2026, 9:11 p.m. |
Created at: April 9, 2026, 5:18 p.m.