Triple
T14723121
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Berlin Station |
E345863
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Mina Tander
Mina Tander is a German actress known for her work in film and television, including prominent roles in international spy and drama series.
|
E1115683
|
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: Mina Tander | Statement: [Berlin Station, starring, Mina Tander]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mina Tander Context triple: [Berlin Station, starring, Mina Tander]
-
A.
Taina Elg
Taina Elg is a Finnish-American actress and dancer best known for her work in mid-20th-century Hollywood musicals and on Broadway.
-
B.
Tania Nell
Tania Nell is a British woman best known as the wife of Olympic distance-running champion Sir Mo Farah.
-
C.
Mialisa Bonta
Mialisa Bonta is an American politician and attorney who serves in the California State Assembly, representing parts of the East Bay.
-
D.
Astrid Menks
Astrid Menks is a Latvian-American philanthropist and former cocktail waitress best known as the longtime partner and later wife of billionaire investor Warren Buffett.
-
E.
Joy Denalane
Joy Denalane is a German soul and R&B singer-songwriter known for blending neo-soul, jazz, and African musical influences in her work.
- 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: Mina Tander Triple: [Berlin Station, starring, Mina Tander]
Generated description
Mina Tander is a German actress known for her work in film and television, including prominent roles in international spy and drama series.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mina Tander Target entity description: Mina Tander is a German actress known for her work in film and television, including prominent roles in international spy and drama series.
-
A.
Taina Elg
Taina Elg is a Finnish-American actress and dancer best known for her work in mid-20th-century Hollywood musicals and on Broadway.
-
B.
Tania Nell
Tania Nell is a British woman best known as the wife of Olympic distance-running champion Sir Mo Farah.
-
C.
Mialisa Bonta
Mialisa Bonta is an American politician and attorney who serves in the California State Assembly, representing parts of the East Bay.
-
D.
Astrid Menks
Astrid Menks is a Latvian-American philanthropist and former cocktail waitress best known as the longtime partner and later wife of billionaire investor Warren Buffett.
-
E.
Joy Denalane
Joy Denalane is a German soul and R&B singer-songwriter known for blending neo-soul, jazz, and African musical influences in her work.
- 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_69d822e5911c8190ba589f957dbd9ba7 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69dec25e9a14819081fa06fc601f295d |
completed | April 14, 2026, 10:40 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fdf0957bb081908f1f382f3be8ec20 |
completed | May 8, 2026, 2:17 p.m. |
| NEDg | Description generation | batch_69fdf440a03c8190886119ab3c8ab610 |
completed | May 8, 2026, 2:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69fdf4f2acbc8190b51ee456093a2813 |
completed | May 8, 2026, 2:36 p.m. |
Created at: April 10, 2026, 1:29 a.m.