Triple
T15340181
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Leod |
E366771
|
entity |
| Predicate | hasLinguisticCategory |
P7162
|
FINISHED |
| Object | anthroponym |
—
|
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: anthroponym | Statement: [Leod, hasLinguisticCategory, anthroponym]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasLinguisticCategory Context triple: [Leod, hasLinguisticCategory, anthroponym]
-
A.
hasLinguisticDomain
Indicates that something (such as a term, expression, or resource) is associated with or applies within a particular linguistic domain or language context.
-
B.
hasLinguisticFeature
chosen
Indicates that an entity possesses a particular linguistic property, trait, or characteristic.
-
C.
hasLinguisticElement
Indicates that one entity includes, is associated with, or is characterized by a particular linguistic component such as a word, phrase, symbol, or other language element.
-
D.
hasLinguisticVariationType
Indicates that one linguistic form is related to another by a specific type of variation, such as dialectal, orthographic, morphological, or phonological difference.
-
E.
hasLinguisticDataType
Indicates that something is associated with or characterized by a specific type or category of linguistic data.
- 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_69d85a1355608190a6673ddb67231d54 |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03e12eb7c8190944a260aa1aa9156 |
completed | April 16, 2026, 1:40 a.m. |
| PD | Predicate disambiguation | batch_69deca9659f48190b8661df223ce5078 |
completed | April 14, 2026, 11:15 p.m. |
Created at: April 10, 2026, 3:17 a.m.