Triple
T14347262
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Weiss |
E355758
|
entity |
| Predicate | componentOfFullName |
P5298
|
FINISHED |
| Object | Weiss |
E68163
|
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: Weiss | Statement: [Hans Weiss, componentOfFullName, Weiss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Weiss Context triple: [Hans Weiss, componentOfFullName, Weiss]
-
A.
Weiss
chosen
Weiss is a common German-language surname borne by numerous notable individuals across fields such as entertainment, science, and politics.
-
B.
Weiss/Manfredi
Weiss/Manfredi is a New York–based architecture and design firm known for its innovative, landscape-integrated cultural and institutional projects.
-
C.
Bianco
Bianco is an Italian surname commonly associated with individuals of Italian heritage, including the artist Enrico Bianco.
-
D.
Branca
Branca is a surname most notably associated with former Major League Baseball pitcher Ralph Branca.
-
E.
Weis
Weis is a surname most prominently associated with Charlie Weis, an American football coach known for his tenure with the Notre Dame Fighting Irish and in the NFL.
- 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_69d82790a7e08190877e2d349b2e8d8e |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de8e8b81bc8190ace2a575faf55cc0 |
completed | April 14, 2026, 6:59 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd4c4145c081909832e2334a064fb0 |
completed | May 8, 2026, 2:36 a.m. |
Created at: April 10, 2026, 1:14 a.m.