Triple
T12435594
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L. Paul Bremer |
E297135
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Bremer |
E936066
|
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: Bremer | Statement: [L. Paul Bremer, familyName, Bremer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bremer Context triple: [L. Paul Bremer, familyName, Bremer]
-
A.
Bremer
chosen
Bremer is a surname most notably associated with American actress and dancer Lucille Bremer, who appeared in classic Hollywood films of the 1940s.
-
B.
Baltus
Baltus is a fictional character best known as the wealthy farmer and father of Katrina Van Tassel in Washington Irving’s short story “The Legend of Sleepy Hollow.”
-
C.
Bremerhaven
Bremerhaven is a major German port city on the North Sea, known for its maritime industry, shipbuilding, and role as a key hub for trade and logistics.
-
D.
Ille
Ille is a small river in northwestern France that flows through the city of Rennes and joins the Vilaine River.
-
E.
Warburg
Warburg is a historic small city in the German state of Hesse, known for its well-preserved medieval old town and hilltop castle.
- 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_69d6ada0640c81908c061d7fb3d47786 |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d94d8c8fd481909b35ac504127a1b6 |
completed | April 10, 2026, 7:20 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f6349f0f34819080e7d7f83f7baece |
completed | May 2, 2026, 5:30 p.m. |
Created at: April 8, 2026, 9:55 p.m.