Triple
T10390
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | English |
E211
|
entity |
| Predicate | hasISO6392Code |
P189
|
FINISHED |
| Object | eng |
—
|
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: eng | Statement: [English, hasISO6392Code, eng]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasISO6392Code Context triple: [English, hasISO6392Code, eng]
-
A.
hasISOCode
chosen
Indicates that an entity is associated with a specific standardized ISO code that uniquely identifies it according to ISO conventions.
-
B.
ISOCode
Indicates that an entity is associated with a specific standardized code defined by the International Organization for Standardization (ISO).
-
C.
hasSignificantLanguage
Indicates that an entity possesses a language that plays an important or primary role in its communication, identity, or functioning.
-
D.
languageOfWorkOrName
Indicates the language in which a work is created or a name is expressed.
-
E.
recognizedLanguage
Indicates that an entity has identified, detected, or acknowledged a particular language as being used or present.
- 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_69a23d7ad88c8190bffe8ab091d86642 |
completed | Feb. 28, 2026, 12:57 a.m. |
| NER | Named-entity recognition | batch_69a242cd8fb481909562f114f4ce7700 |
completed | Feb. 28, 2026, 1:20 a.m. |
| PD | Predicate disambiguation | batch_69a23fe6b0bc8190bcce9b74f2c5fb08 |
completed | Feb. 28, 2026, 1:07 a.m. |
Created at: Feb. 28, 2026, 1:02 a.m.