Triple
T10039694
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Clemons |
E205261
|
entity |
| Predicate | hasVariant |
P455
|
FINISHED |
| Object | Clements |
E747797
|
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: Clements | Statement: [Clemons, hasVariant, Clements]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clements Context triple: [Clemons, hasVariant, Clements]
-
A.
Clements
chosen
Clements is a surname most notably associated with American plant ecologist Frederic Clements, a pioneer in the study of ecological succession.
-
B.
Clem
Clem is a short, informal given name typically used as a diminutive of Clement.
-
C.
Mathieson
Mathieson is a surname of Scottish origin, typically regarded as a variant of Matheson and derived from a patronymic meaning “son of Matthew.”
-
D.
Cressner
Cressner is the sadistic, wealthy gambler and primary villain in Stephen King’s short story “The Ledge,” known for forcing a man to risk his life by walking around a narrow ledge high above the city.
-
E.
Bonger
Bonger is a Dutch surname most notably associated with Johanna van Gogh-Bonger, the key figure in preserving and promoting Vincent van Gogh’s artistic legacy.
- 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_69ca834f70e88190b2d74828b7767ec1 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cdcee186708190bc9fecd637b4f7e6 |
completed | April 2, 2026, 2:05 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d28268ab648190a565472d00b289c2 |
completed | April 5, 2026, 3:40 p.m. |
Created at: March 30, 2026, 8:55 p.m.