Triple
T7744674
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Aloysia Weber |
E175596
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Weber |
E154323
|
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: Weber | Statement: [Aloysia Weber, familyName, Weber]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weber Context triple: [Aloysia Weber, familyName, Weber]
-
A.
Weber
chosen
Weber is a common German surname borne by numerous notable individuals across fields such as sociology, music, and politics.
-
B.
Weinert
Weinert is a German-language surname borne by various notable individuals in fields such as the arts, sciences, and public life.
-
C.
Seelbach
Seelbach is a municipality in southwestern Germany’s Baden-Württemberg region, situated in the Ortenau district near the Black Forest.
-
D.
Wiebe
Wiebe is a given name and surname of Frisian and Dutch origin, used in various forms across the Netherlands and surrounding regions.
-
E.
Muffendorf
Muffendorf is a historic, village-like residential quarter in the Bonn district of Bad Godesberg, known for its traditional half-timbered houses and picturesque setting.
- 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_69c69960b3588190a53aa590d31d9544 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c70389e9548190a57b5370f4b9fee9 |
completed | March 27, 2026, 10:24 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c8be4cdb148190af3ebaf9ac35b641 |
completed | March 29, 2026, 5:53 a.m. |
Created at: March 27, 2026, 4:07 p.m.