Triple
T625203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. President |
E15801
|
entity |
| Predicate | grammaticalCategory |
P12863
|
FINISHED |
| Object | noun phrase |
—
|
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: noun phrase | Statement: [Mr. President, grammaticalCategory, noun phrase]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grammaticalCategory Context triple: [Mr. President, grammaticalCategory, noun phrase]
-
A.
grammaticalType
chosen
Indicates the grammatical category or role (such as part of speech or syntactic function) that an expression has within a language.
-
B.
grammaticalForm
Indicates the specific grammatical structure or morphological form that an expression or word takes in a given linguistic context.
-
C.
primaryGrammaticalBasis
Indicates that one element serves as the main grammatical foundation or core structure upon which another linguistic element is based or constructed.
-
D.
grammaticalStructure
Indicates the way linguistic elements are organized and related within a sentence or phrase according to grammatical rules.
-
E.
hasGrammaticalGender
Indicates that one entity assigns or possesses a specific grammatical gender in relation to another entity (such as a word, phrase, or linguistic unit).
- 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_69a4935c131c8190a5378c6bf101e8cc |
completed | March 1, 2026, 7:28 p.m. |
| NER | Named-entity recognition | batch_69a49e574444819087999404f3e3ffd9 |
completed | March 1, 2026, 8:15 p.m. |
| PD | Predicate disambiguation | batch_69a49d0069d0819087c83b608f6fc053 |
completed | March 1, 2026, 8:09 p.m. |
Created at: March 1, 2026, 7:35 p.m.