Triple
T4508447
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maja Einstein |
E101989
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Maja
Maja was the younger sister of physicist Albert Einstein, known for her close relationship with him and her background in literature and languages.
|
E448720
|
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: Maja | Statement: [Maja Einstein, nickname, Maja]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maja Context triple: [Maja Einstein, nickname, Maja]
-
A.
Marga
Marga is a feminine given name, commonly used as a short or diminutive form of names like Margarita or Margareta.
-
B.
Majkin
Majkin is a small settlement located on Namu Atoll in the Marshall Islands, likely serving as one of the atoll’s primary residential communities.
-
C.
Lujza
Lujza is a given name, primarily used in Central and Eastern Europe, that corresponds to the name Luisa or Louise in other languages.
-
D.
Maja e Lubotenit
Maja e Lubotenit is a prominent peak in the Šar Mountains on the border of Kosovo and North Macedonia, known for its scenic alpine landscapes and popular hiking routes.
-
E.
Neilia
Neilia was an American educator best known as the first wife of Joe Biden, who tragically died in a car accident in 1972 along with their infant daughter.
- 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: Maja Triple: [Maja Einstein, nickname, Maja]
Generated description
Maja was the younger sister of physicist Albert Einstein, known for her close relationship with him and her background in literature and languages.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maja Target entity description: Maja was the younger sister of physicist Albert Einstein, known for her close relationship with him and her background in literature and languages.
-
A.
Marga
Marga is a feminine given name, commonly used as a short or diminutive form of names like Margarita or Margareta.
-
B.
Majkin
Majkin is a small settlement located on Namu Atoll in the Marshall Islands, likely serving as one of the atoll’s primary residential communities.
-
C.
Lujza
Lujza is a given name, primarily used in Central and Eastern Europe, that corresponds to the name Luisa or Louise in other languages.
-
D.
Maja e Lubotenit
Maja e Lubotenit is a prominent peak in the Šar Mountains on the border of Kosovo and North Macedonia, known for its scenic alpine landscapes and popular hiking routes.
-
E.
Neilia
Neilia was an American educator best known as the first wife of Joe Biden, who tragically died in a car accident in 1972 along with their infant daughter.
- 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_69bd43d6251c81909deecce3e6e9d69c |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd570f9b1c8190b52ace855dbf6641 |
completed | March 20, 2026, 2:17 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bd7f760658819085ca8c631894b486 |
completed | March 20, 2026, 5:10 p.m. |
| NEDg | Description generation | batch_69bd860cb500819082e22070713ed2d4 |
completed | March 20, 2026, 5:38 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69bd867ee76c81909241816ac2045451 |
completed | March 20, 2026, 5:40 p.m. |
Created at: March 20, 2026, 1:01 p.m.