Triple
T11598635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lucille Bremer |
E275068
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Bremer
Bremer is a surname most notably associated with American actress and dancer Lucille Bremer, who appeared in classic Hollywood films of the 1940s.
|
E936066
|
NE FINISHED |
How this triple was built (4 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: [Lucille Bremer, familyName, Bremer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bremer Context triple: [Lucille Bremer, familyName, Bremer]
-
A.
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.”
-
B.
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.
-
C.
Ille
Ille is a small river in northwestern France that flows through the city of Rennes and joins the Vilaine River.
-
D.
Warburg
Warburg is a historic small city in the German state of Hesse, known for its well-preserved medieval old town and hilltop castle.
-
E.
Warburg
Warburg is a prominent German-Jewish banking and philanthropic family historically influential in international finance and economic policy.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Bremer Triple: [Lucille Bremer, familyName, Bremer]
Generated description
Bremer is a surname most notably associated with American actress and dancer Lucille Bremer, who appeared in classic Hollywood films of the 1940s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bremer Target entity description: Bremer is a surname most notably associated with American actress and dancer Lucille Bremer, who appeared in classic Hollywood films of the 1940s.
-
A.
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.”
-
B.
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.
-
C.
Ille
Ille is a small river in northwestern France that flows through the city of Rennes and joins the Vilaine River.
-
D.
Warburg
Warburg is a historic small city in the German state of Hesse, known for its well-preserved medieval old town and hilltop castle.
-
E.
Warburg
Warburg is a prominent German-Jewish banking and philanthropic family historically influential in international finance and economic policy.
- F. None of above. chosen
Provenance (5 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8954c3c248190bcccd4c7ff667b3a |
completed | April 10, 2026, 6:14 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e8a7dd83d48190b281a6fcfc3e4087 |
completed | April 22, 2026, 10:50 a.m. |
| NEDg | Description generation | batch_69e8af93e07c8190aecb040cac6db146 |
completed | April 22, 2026, 11:23 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69ee5b254a2081909cba97a6ecb10601 |
completed | April 26, 2026, 6:36 p.m. |
Created at: April 8, 2026, 9:38 p.m.