Triple
T17703552
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Annette Strauss Artist Square |
E441370
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Annette Strauss |
—
|
NE NERFINISHED |
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: Annette Strauss | Statement: [Annette Strauss Artist Square, namedAfter, Annette Strauss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Annette Strauss Context triple: [Annette Strauss Artist Square, namedAfter, Annette Strauss]
-
A.
Annette Strauss
chosen
Annette Strauss was an American civic leader and philanthropist who became one of Dallas’s most prominent mayors in the late 1980s.
-
B.
Annette Ziegler
Annette Ziegler is an American jurist who serves as the Chief Justice of the Wisconsin Supreme Court.
-
C.
Fran Weissler
Fran Weissler is a prominent American Broadway producer known for her award-winning revivals and long-running hits alongside her husband, Barry Weissler.
-
D.
Barbara Steiner
Barbara Steiner is a fictional character in Markus Zusak's novel "The Book Thief," known as the hardworking and often strict mother of Rudy Steiner in Nazi Germany.
-
E.
Annette Stroyberg
Annette Stroyberg was a Danish actress and model best known for her roles in European films of the late 1950s and 1960s.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8b9ea20b48190ace88bb46b01e6a9 |
completed | April 10, 2026, 8:50 a.m. |
| NER | Named-entity recognition | batch_69e4729528b88190bd8a104f6f6d4e69 |
completed | April 19, 2026, 6:13 a.m. |
Created at: April 10, 2026, 10:05 a.m.