Triple
T11844765
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Professor Marvel |
E281745
|
entity |
| Predicate | associatedWith |
P37
|
FINISHED |
| Object | Kansas |
E30311
|
NE FINISHED |
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: Kansas | Statement: [Professor Marvel, associatedWith, Kansas]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Kansas Context triple: [Professor Marvel, associatedWith, Kansas]
-
A.
Kansas
chosen
Kansas is a largely rural, landlocked U.S. state known for its extensive plains, agricultural production, and central location within the country.
-
B.
Kansas, Georgia
Kansas, Georgia is a small unincorporated rural community located in Carroll County in the western part of the state.
-
C.
Kansas, Alabama
Kansas, Alabama is a small unincorporated rural community located in Walker County in the U.S. state of Alabama.
-
D.
Nebraska
Nebraska is a landlocked U.S. state on the Great Plains known for its agriculture, prairies, and role as a historic crossroads for westward expansion.
-
E.
Nebraska
Nebraska is a 2013 black-and-white American road comedy-drama film directed by Alexander Payne that follows an aging man's quixotic journey to claim a supposed sweepstakes prize.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69d6ab287ba48190a5178779fd19b9b7 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8a65b5ff08190bb58361f6a6acdca |
completed | April 10, 2026, 7:27 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f2814210e48190821fca390dc7e312 |
completed | April 29, 2026, 10:08 p.m. |
Created at: April 8, 2026, 9:43 p.m.