Triple
T20404919
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fuchsian singularity |
E500440
|
entity |
| Predicate | hasTerminologyVariant |
P457
|
FINISHED |
| Object | Fuchsian singular point |
—
|
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: Fuchsian singular point | Statement: [Fuchsian singularity, hasTerminologyVariant, Fuchsian singular point]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTerminologyVariant Context triple: [Fuchsian singularity, hasTerminologyVariant, Fuchsian singular point]
-
A.
hasLinguisticVariationType
Indicates that one linguistic form is related to another by a specific type of variation, such as dialectal, orthographic, morphological, or phonological difference.
-
B.
usesTerminologyFrom
Indicates that one entity adopts or incorporates the specialized terms or vocabulary originating from another entity or source.
-
C.
hasLinguisticVariety
Indicates that one entity possesses or exhibits a particular linguistic variety in relation to another entity or context.
-
D.
hasAcronymVariantLanguage
Indicates that a language has an alternative form represented as an acronym variant.
-
E.
hasVariantSpelling
chosen
Indicates that one term is an alternative spelling form of another term.
- 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_69e0b4a81bec8190b69adfdc1336a015 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6799161c48190825eca3027d1aa51 |
completed | April 20, 2026, 7:08 p.m. |
| PD | Predicate disambiguation | batch_69e5765d7cb48190adec18d6d1e3d263 |
completed | April 20, 2026, 12:42 a.m. |
Created at: April 16, 2026, 11:29 a.m.