Triple
T6668452
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Standard Moroccan Amazigh |
E151663
|
entity |
| Predicate | ISO639_2Code |
P5197
|
FINISHED |
| Object | zgh |
—
|
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: zgh | Statement: [Standard Moroccan Amazigh, ISO639_2Code, zgh]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: ISO639_2Code Context triple: [Standard Moroccan Amazigh, ISO639_2Code, zgh]
-
A.
ISO639-3CodeOfLanguage
Indicates that one entity is the ISO 639-3 three-letter language code assigned to the language represented by the other entity.
-
B.
sharesISO639-3CodeWith
Indicates that two language entities share the same ISO 639-3 code, meaning they are treated as the same language in that coding system.
-
C.
languageCodeISO639-2
chosen
Indicates that an entity is associated with a language identified by its ISO 639-2 three-letter code.
-
D.
ISO639CollectiveCode
Indicates that the relationship assigns or associates an ISO 639 collective language code (a code representing a group of related languages) to the relevant language entity or set of languages.
-
E.
ISO639-6Code
Indicates the standardized ISO 639-6 four-letter code that uniquely identifies a specific language variety or dialect.
- 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_69c687f71fc081909dbd45d6377f6045 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6ce738fe88190a5557900efeec7ec |
completed | March 27, 2026, 6:37 p.m. |
| PD | Predicate disambiguation | batch_69c6ad09974c81908784300ae218961f |
completed | March 27, 2026, 4:15 p.m. |
Created at: March 27, 2026, 2:02 p.m.