Triple
T12132880
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haldemann |
E288976
|
entity |
| Predicate | hasSpellingVariantPattern |
P457
|
FINISHED |
| Object | single-n vs double-n surname ending |
—
|
LITERAL 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: single-n vs double-n surname ending | Statement: [Haldemann, hasSpellingVariantPattern, single-n vs double-n surname ending]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSpellingVariantPattern Context triple: [Haldemann, hasSpellingVariantPattern, single-n vs double-n surname ending]
-
A.
hasVariantSpelling
chosen
Indicates that one term is an alternative spelling form of another term.
-
B.
hasVariantText
Indicates that an entity is associated with an alternative or differing textual form of its content.
-
C.
hasTypicalSpelling
Indicates that one form is the standard or commonly accepted spelling of another form.
-
D.
hasVariantReadingsWith
Indicates a relationship where two textual items are linked because they exhibit differing or alternative readings of (typically) the same underlying content.
-
E.
hasVariant
Indicates that one entity exists as an alternative form, version, or variation of another entity.
- F. None of above.
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_69d6ab4b5e4c81909950b17151eb0951 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d91841615c819097f20a7447a1b8f4 |
completed | April 10, 2026, 3:33 p.m. |
| PD | Predicate disambiguation | batch_69d91508f8008190b3a90ec0bf0953ca |
completed | April 10, 2026, 3:19 p.m. |
Created at: April 8, 2026, 9:49 p.m.