Triple
T8885306
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | William Barfée |
E211513
|
entity |
| Predicate | spellingTechnique |
P85530
|
FINISHED |
| Object | tracing letters with his foot on the floor |
—
|
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: tracing letters with his foot on the floor | Statement: [William Barfée, spellingTechnique, tracing letters with his foot on the floor]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spellingTechnique Context triple: [William Barfée, spellingTechnique, tracing letters with his foot on the floor]
-
A.
spellingGimmick
Indicates a distinctive or unconventional way of spelling something used for effect or branding rather than standard orthography.
-
B.
syllabarySpelling
Indicates how a word or term is written using a syllabary-based writing system rather than an alphabetic one.
-
C.
spellingStability
Indicates the degree to which the spelling of a word or term remains consistent over time or across different uses.
-
D.
spellingStatus
Indicates the correctness or condition of the spelling of a given text or term.
-
E.
effectOnSpelling
Indicates a relationship where one factor influences or alters the way something is spelled.
- F. None of above. chosen
Provenance (4 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_69ca838f9e20819096ab1f236a70381a |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc616cf8c48190a27b381e48f23377 |
completed | April 1, 2026, 12:06 a.m. |
| PD | Predicate disambiguation | batch_69cc5c2aec04819093c932fe51c0f08d |
completed | March 31, 2026, 11:43 p.m. |
| PDg | Predicate description generation | batch_69cc5d6e54808190af4156edd4c8ffbc |
completed | March 31, 2026, 11:49 p.m. |
Created at: March 30, 2026, 6:53 p.m.