Triple
T4154127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | National Institute for Medical Research |
E89974
|
entity |
| Predicate | employerOf |
P7
|
FINISHED |
| Object |
Brenda Rappaport
Brenda Rappaport is a medical researcher associated with the United Kingdom’s National Institute for Medical Research.
|
E491862
|
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: Brenda Rappaport | Statement: [National Institute for Medical Research, employerOf, Brenda Rappaport]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Brenda Rappaport Context triple: [National Institute for Medical Research, employerOf, Brenda Rappaport]
-
A.
June Preisser
June Preisser was an American film actress and dancer best known for her energetic supporting roles in 1930s and 1940s Hollywood musicals, often playing peppy, acrobatic teenagers.
-
B.
Brenda Vaccaro
Brenda Vaccaro is an American actress known for her distinctive husky voice and acclaimed performances in film, television, and theater since the 1960s.
-
C.
Joanne Brenner
Joanne Brenner is the mother of American actress Alison Brie.
-
D.
Barbara Bosson
Barbara Bosson was an American actress best known for her Emmy-nominated role as Fay Furillo on the groundbreaking police drama "Hill Street Blues."
-
E.
Roberta Seidman
Roberta Seidman was the wife of American actor John Garfield, a prominent film star of the 1930s and 1940s.
- 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: Brenda Rappaport Triple: [National Institute for Medical Research, employerOf, Brenda Rappaport]
Generated description
Brenda Rappaport is a medical researcher associated with the United Kingdom’s National Institute for Medical Research.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Brenda Rappaport Target entity description: Brenda Rappaport is a medical researcher associated with the United Kingdom’s National Institute for Medical Research.
-
A.
June Preisser
June Preisser was an American film actress and dancer best known for her energetic supporting roles in 1930s and 1940s Hollywood musicals, often playing peppy, acrobatic teenagers.
-
B.
Brenda Vaccaro
Brenda Vaccaro is an American actress known for her distinctive husky voice and acclaimed performances in film, television, and theater since the 1960s.
-
C.
Joanne Brenner
Joanne Brenner is the mother of American actress Alison Brie.
-
D.
Barbara Bosson
Barbara Bosson was an American actress best known for her Emmy-nominated role as Fay Furillo on the groundbreaking police drama "Hill Street Blues."
-
E.
Roberta Seidman
Roberta Seidman was the wife of American actor John Garfield, a prominent film star of the 1930s and 1940s.
- 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_69aed95a59a881909b26e70b42c6811a |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69af027954008190a28841802055afe8 |
completed | March 9, 2026, 5:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69beb0b17e288190b6014ad9c31a3b9f |
completed | March 21, 2026, 2:52 p.m. |
| NEDg | Description generation | batch_69beb16170408190a04dded7fcc512d8 |
completed | March 21, 2026, 2:55 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69beb1c3bc5c8190b8a58baf2cd1ad44 |
completed | March 21, 2026, 2:57 p.m. |
Created at: March 9, 2026, 3:44 p.m.