Triple
T4971536
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sheldon, Iowa |
E111662
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object |
Israel Sheldon
Israel Sheldon was an early settler and prominent local figure after whom the city of Sheldon, Iowa, was named.
|
E483730
|
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: Israel Sheldon | Statement: [Sheldon, Iowa, namedAfter, Israel Sheldon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Israel Sheldon Context triple: [Sheldon, Iowa, namedAfter, Israel Sheldon]
-
A.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
B.
Chris Lebenzon
Chris Lebenzon is an American film editor known for his long-time collaborations with directors like Tim Burton and Tony Scott on major Hollywood films.
-
C.
Andrew Mondshein
Andrew Mondshein is an American film editor known for his work on acclaimed movies such as "Ma Rainey's Black Bottom" and "The Sixth Sense."
-
D.
Jay Rabinowitz
Jay Rabinowitz is a film editor known for his work on numerous feature films, including the science-fiction thriller "The Adjustment Bureau."
-
E.
Uriel Frisch
Uriel Frisch is a French physicist and mathematician renowned for his contributions to fluid dynamics and turbulence theory.
- 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: Israel Sheldon Triple: [Sheldon, Iowa, namedAfter, Israel Sheldon]
Generated description
Israel Sheldon was an early settler and prominent local figure after whom the city of Sheldon, Iowa, was named.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Israel Sheldon Target entity description: Israel Sheldon was an early settler and prominent local figure after whom the city of Sheldon, Iowa, was named.
-
A.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
B.
Chris Lebenzon
Chris Lebenzon is an American film editor known for his long-time collaborations with directors like Tim Burton and Tony Scott on major Hollywood films.
-
C.
Andrew Mondshein
Andrew Mondshein is an American film editor known for his work on acclaimed movies such as "Ma Rainey's Black Bottom" and "The Sixth Sense."
-
D.
Jay Rabinowitz
Jay Rabinowitz is a film editor known for his work on numerous feature films, including the science-fiction thriller "The Adjustment Bureau."
-
E.
Uriel Frisch
Uriel Frisch is a French physicist and mathematician renowned for his contributions to fluid dynamics and turbulence theory.
- 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_69bd441a0eb481908050fa4273b19eae |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd7213bf0081909b3c496f1804dc4c |
completed | March 20, 2026, 4:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be81fa9108819089a6258e3a88f0cb |
completed | March 21, 2026, 11:33 a.m. |
| NEDg | Description generation | batch_69be8386d2fc8190a450b42dd5ac6963 |
completed | March 21, 2026, 11:39 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69be841d148881908aa53953bd2eb024 |
completed | March 21, 2026, 11:42 a.m. |
Created at: March 20, 2026, 1:33 p.m.