Triple
T11466652
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lowenstein–Jensen medium |
E271796
|
entity |
| Predicate | developedBy |
P73
|
FINISHED |
| Object |
Kurt Jensen
Kurt Jensen was a microbiologist known for co-developing the Lowenstein–Jensen medium, a key culture medium used in the diagnosis of tuberculosis.
|
E940863
|
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: Kurt Jensen | Statement: [Lowenstein–Jensen medium, developedBy, Kurt Jensen]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kurt Jensen Context triple: [Lowenstein–Jensen medium, developedBy, Kurt Jensen]
-
A.
Kurt Johnstad
Kurt Johnstad is an American screenwriter best known for writing the action films "300" and "Atomic Blonde."
-
B.
Ron Jensen
Ron Jensen is an American politician who has served as the mayor of Grand Prairie, Texas.
-
C.
Jon Jensen
Jon Jensen is the central protagonist of the film "The Salvation," around whom the story’s dramatic events and conflicts revolve.
-
D.
Warren Skaaren
Warren Skaaren was an American screenwriter and script doctor best known for his work on major 1980s films such as "Beetlejuice" and "Batman."
-
E.
Ben F. Jensen
Ben F. Jensen was a U.S. Congressman who was wounded during the 1954 shooting attack in the House of Representatives chamber.
- 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: Kurt Jensen Triple: [Lowenstein–Jensen medium, developedBy, Kurt Jensen]
Generated description
Kurt Jensen was a microbiologist known for co-developing the Lowenstein–Jensen medium, a key culture medium used in the diagnosis of tuberculosis.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Kurt Jensen Target entity description: Kurt Jensen was a microbiologist known for co-developing the Lowenstein–Jensen medium, a key culture medium used in the diagnosis of tuberculosis.
-
A.
Kurt Johnstad
Kurt Johnstad is an American screenwriter best known for writing the action films "300" and "Atomic Blonde."
-
B.
Ron Jensen
Ron Jensen is an American politician who has served as the mayor of Grand Prairie, Texas.
-
C.
Jon Jensen
Jon Jensen is the central protagonist of the film "The Salvation," around whom the story’s dramatic events and conflicts revolve.
-
D.
Warren Skaaren
Warren Skaaren was an American screenwriter and script doctor best known for his work on major 1980s films such as "Beetlejuice" and "Batman."
-
E.
Ben F. Jensen
Ben F. Jensen was a U.S. Congressman who was wounded during the 1954 shooting attack in the House of Representatives chamber.
- 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_69d6aae0c8d881908a5a360c0be3242e |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d822f5eb988190b309b8e309f6d1a5 |
completed | April 9, 2026, 10:06 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ef12c1cd048190b58410542005acbc |
completed | April 27, 2026, 7:39 a.m. |
| NEDg | Description generation | batch_69ef511f8f688190b2806d4e8ab16511 |
completed | April 27, 2026, 12:05 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ef537efcc48190afffaa50f28940d8 |
completed | April 27, 2026, 12:15 p.m. |
Created at: April 8, 2026, 9:35 p.m.