Triple
T19829793
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Christine Baumgartner |
E476423
|
entity |
| Predicate | businessPartner |
P282
|
FINISHED |
| Object |
Tamara Muro
Tamara Muro is a business associate of Christine Baumgartner, known in connection with Baumgartner’s professional ventures.
|
E1404295
|
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: Tamara Muro | Statement: [Christine Baumgartner, businessPartner, Tamara Muro]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tamara Muro Context triple: [Christine Baumgartner, businessPartner, Tamara Muro]
-
A.
Tamara Braun
Tamara Braun is an American actress best known for her Emmy-winning work on daytime soap operas, including prominent roles on "General Hospital" and "Days of Our Lives."
-
B.
Tamara Lang
Tamara Lang is known as the spouse of Michael Lang, the co-creator and promoter of the original Woodstock music festival.
-
C.
Tamara Motyleva
Tamara Motyleva is a screenwriter best known for her work on the 1956 Soviet film "Mother," an adaptation of Maxim Gorky's novel.
-
D.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
E.
Liliana Mumy
Liliana Mumy is an American actress and voice actress known for her roles in family films and animated television series such as "Cheaper by the Dozen" and "The Loud House."
- 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: Tamara Muro Triple: [Christine Baumgartner, businessPartner, Tamara Muro]
Generated description
Tamara Muro is a business associate of Christine Baumgartner, known in connection with Baumgartner’s professional ventures.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tamara Muro Target entity description: Tamara Muro is a business associate of Christine Baumgartner, known in connection with Baumgartner’s professional ventures.
-
A.
Tamara Braun
Tamara Braun is an American actress best known for her Emmy-winning work on daytime soap operas, including prominent roles on "General Hospital" and "Days of Our Lives."
-
B.
Tamara Lang
Tamara Lang is known as the spouse of Michael Lang, the co-creator and promoter of the original Woodstock music festival.
-
C.
Tamara Motyleva
Tamara Motyleva is a screenwriter best known for her work on the 1956 Soviet film "Mother," an adaptation of Maxim Gorky's novel.
-
D.
Tamara
Tamara is a feminine given name of Hebrew origin, commonly used in various cultures and languages.
-
E.
Liliana Mumy
Liliana Mumy is an American actress and voice actress known for her roles in family films and animated television series such as "Cheaper by the Dozen" and "The Loud House."
- 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_69d8e51c7c188190b926f3a2a7b5f881 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e656ccd3748190adeaed9a431f8979 |
completed | April 20, 2026, 4:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07fdb975548190b83292a14fded6a1 |
completed | May 16, 2026, 5:16 a.m. |
| NEDg | Description generation | batch_6a07fe81005081909b70102524cb8fda |
completed | May 16, 2026, 5:20 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a07feeec210819084d6a6cf472fc45f |
completed | May 16, 2026, 5:21 a.m. |
Created at: April 10, 2026, 1:50 p.m.