Triple
T18823678
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pepper |
E460328
|
entity |
| Predicate | hasNotableBearer |
P458
|
FINISHED |
| Object |
Claude Pepper
Claude Pepper was a long-serving American politician from Florida known for his advocacy of New Deal policies, social welfare programs, and the rights of the elderly.
|
E1344966
|
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: Claude Pepper | Statement: [Pepper, hasNotableBearer, Claude Pepper]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Claude Pepper Context triple: [Pepper, hasNotableBearer, Claude Pepper]
-
A.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on the acclaimed drama "Glengarry Glen Ross."
-
B.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on the thriller "Snakes on a Plane."
-
C.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on major Hollywood productions, including the shark thriller "Deep Blue Sea."
-
D.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on major Hollywood productions, including the action thriller "Point Break."
-
E.
Howard E. Smith
Howard E. Smith was a film editor best known for his work on notable movies such as "Strange Days."
- 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: Claude Pepper Triple: [Pepper, hasNotableBearer, Claude Pepper]
Generated description
Claude Pepper was a long-serving American politician from Florida known for his advocacy of New Deal policies, social welfare programs, and the rights of the elderly.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Claude Pepper Target entity description: Claude Pepper was a long-serving American politician from Florida known for his advocacy of New Deal policies, social welfare programs, and the rights of the elderly.
-
A.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on the acclaimed drama "Glengarry Glen Ross."
-
B.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on the thriller "Snakes on a Plane."
-
C.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on major Hollywood productions, including the shark thriller "Deep Blue Sea."
-
D.
Howard E. Smith
Howard E. Smith is a film editor best known for his work on major Hollywood productions, including the action thriller "Point Break."
-
E.
Howard E. Smith
Howard E. Smith was a film editor best known for his work on notable movies such as "Strange Days."
- 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_69d8dcf94c288190a06dea029ae4b223 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5a6bce5588190bd0aefcd0c51edad |
completed | April 20, 2026, 4:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a055bcea4fc8190bc9b9e23b1925b6f |
completed | May 14, 2026, 5:21 a.m. |
| NEDg | Description generation | batch_6a055d25d9608190b70473a28a7d4333 |
completed | May 14, 2026, 5:27 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a055dbba9a481909a8c7c4c6bdab27f |
completed | May 14, 2026, 5:29 a.m. |
Created at: April 10, 2026, 11:56 a.m.