Triple
T64325
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | L'union fait la force |
E1278
|
entity |
| Predicate | grammaticalStructure |
P4412
|
FINISHED |
| Object | declarative sentence |
—
|
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: declarative sentence | Statement: [L'union fait la force, grammaticalStructure, declarative sentence]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: grammaticalStructure Context triple: [L'union fait la force, grammaticalStructure, declarative sentence]
-
A.
sentence
Indicates that one entity is a sentence that expresses, contains, or encodes information about another entity.
-
B.
hasBasicWordOrder
Indicates the typical sequence in which core sentence elements (such as subject, verb, and object) are ordered in a language.
-
C.
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).
-
D.
namingStructure
Indicates a relationship where one entity defines, organizes, or constrains the naming or label format used for another entity.
-
E.
writtenForm
Indicates that one entity is the textual or orthographic representation (spelling or written version) of another entity.
- 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_69a24ba4f760819081f6638a3c70538a |
completed | Feb. 28, 2026, 1:57 a.m. |
| NER | Named-entity recognition | batch_69a2516eda54819090f5c14384d4eab1 |
completed | Feb. 28, 2026, 2:22 a.m. |
| PD | Predicate disambiguation | batch_69a24ea5c140819080409a968c8d2ce8 |
completed | Feb. 28, 2026, 2:10 a.m. |
| PDg | Predicate description generation | batch_69a2516d98e88190b79261bd3fcadd9b |
completed | Feb. 28, 2026, 2:22 a.m. |
Created at: Feb. 28, 2026, 2:02 a.m.