Triple
T19340164
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Israel Sheldon |
E483730
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Israel Sheldon |
—
|
NE NERFINISHED |
How this triple was built (2 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: [Israel Sheldon, name, Israel Sheldon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Israel Sheldon Context triple: [Israel Sheldon, name, Israel Sheldon]
-
A.
Israel Sheldon
chosen
Israel Sheldon was an early settler and prominent local figure after whom the city of Sheldon, Iowa, was named.
-
B.
Jesse Landau
Jesse Landau is known primarily as the child of acclaimed American film producer Jon Landau, associated with blockbuster films like Titanic and Avatar.
-
C.
Nathan Schiff
Nathan Schiff is an American underground filmmaker best known for his ultra-low-budget, gore-heavy horror movies that gained a cult following.
-
D.
Ali Weinberg
Ali Weinberg is an American journalist and television news producer known for her work covering politics for major U.S. news networks.
-
E.
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.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8d244f8819080eb1f3491300db2 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e618560f0081908565f802ea1e3cc8 |
completed | April 20, 2026, 12:13 p.m. |
Created at: April 10, 2026, 1:33 p.m.