Triple
T4372523
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Zimmermann |
E98929
|
entity |
| Predicate | spellingCharacteristic |
P6520
|
FINISHED |
| Object | contains double "n" at the end |
—
|
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: contains double "n" at the end | Statement: [Zimmermann, spellingCharacteristic, contains double "n" at the end]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spellingCharacteristic Context triple: [Zimmermann, spellingCharacteristic, contains double "n" at the end]
-
A.
spellingStability
Indicates the degree to which the spelling of a word or term remains consistent over time or across different uses.
-
B.
spellingGimmick
Indicates a distinctive or unconventional way of spelling something used for effect or branding rather than standard orthography.
-
C.
effectOnSpelling
Indicates a relationship where one factor influences or alters the way something is spelled.
-
D.
sharesSpellingWith
Indicates that two entities have identical or substantially identical written forms (i.e., they are spelled the same way).
-
E.
linguisticFeature
chosen
Indicates a relationship where a linguistic property, pattern, or characteristic is attributed to or associated with a language-related entity (such as a word, phrase, or text).
- 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_69b3454db3708190aeafd814413c4c3d |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b3521f7d9c81909c9209fe59d20ffd |
completed | March 12, 2026, 11:54 p.m. |
| PD | Predicate disambiguation | batch_69b34f557fe8819085032bf7f0cea5dc |
completed | March 12, 2026, 11:42 p.m. |
Created at: March 12, 2026, 11:17 p.m.