Triple
T16391608
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fabricio Werdum |
E398065
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Werdum |
E627984
|
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: Werdum | Statement: [Fabricio Werdum, familyName, Werdum]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Werdum Context triple: [Fabricio Werdum, familyName, Werdum]
-
A.
Werdum
chosen
Werdum is a small village and tourist destination in East Frisia in northwestern Germany, known for its rural charm and proximity to the North Sea coast.
-
B.
Durkan
Durkan is a surname most notably associated with Jenny Durkan, the former mayor of Seattle and an American attorney and politician.
-
C.
Rodemack
Rodemack is a historic fortified village in northeastern France, renowned for its well-preserved medieval ramparts and picturesque old town.
-
D.
Korenlei
Korenlei is a historic quay along the Leie River in Ghent, Belgium, known for its picturesque medieval guild houses and waterfront views.
-
E.
Tyrš
Tyrš is a Czech surname most notably associated with Miroslav Tyrš, a key founder of the Sokol physical education movement.
- 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_69d87f2880b48190ae1a9673a3bbef80 |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e326436ce881909f0f3899b8d931aa |
completed | April 18, 2026, 6:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a003573c6d48190979201b9619c5103 |
completed | May 10, 2026, 7:36 a.m. |
Created at: April 10, 2026, 5:08 a.m.