Triple
T2840811
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Marian |
E62460
|
entity |
| Predicate | linguisticCategory |
P10465
|
FINISHED |
| Object | proper noun |
—
|
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: proper noun | Statement: [Marian, linguisticCategory, proper noun]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: linguisticCategory Context triple: [Marian, linguisticCategory, proper noun]
-
A.
linguisticClassification
chosen
Indicates the relationship by which an entity is categorized according to its language or linguistic type.
-
B.
linguisticType
Indicates the type or category of language or linguistic system associated with an entity (e.g., spoken, signed, written, or other linguistic modality).
-
C.
linguisticFeature
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).
-
D.
glottoCategory
Indicates the linguistic classification or type (such as language family, subgroup, or category) to which a language or dialect is assigned.
-
E.
linguisticField
Indicates that something pertains to or is associated with a particular area or subdiscipline within linguistics.
- 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_69ab4c3d16bc81908b3a1c98fbd287fe |
completed | March 6, 2026, 9:50 p.m. |
| NER | Named-entity recognition | batch_69abdf15b7288190a03d1193cc0544a6 |
completed | March 7, 2026, 8:17 a.m. |
| PD | Predicate disambiguation | batch_69abdd0ce8b08190ba28c192988f38ce |
completed | March 7, 2026, 8:08 a.m. |
Created at: March 6, 2026, 10:01 p.m.