Triple
T4308042
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FK |
E94003
|
entity |
| Predicate | languageCodeRelation |
P56371
|
FINISHED |
| Object | not an ISO 639 language code |
—
|
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: not an ISO 639 language code | Statement: [FK, languageCodeRelation, not an ISO 639 language code]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageCodeRelation Context triple: [FK, languageCodeRelation, not an ISO 639 language code]
-
A.
languageCodeISO639-2
Indicates that an entity is associated with a language identified by its ISO 639-2 three-letter code.
-
B.
ISO639-3CodeOfLanguage
Indicates that one entity is the ISO 639-3 three-letter language code assigned to the language represented by the other entity.
-
C.
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.
-
D.
languageCodeISO639-1
Indicates that the subject entity is associated with the specified two-letter ISO 639-1 language code.
-
E.
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.
- 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_69b3451886588190a3dd1305ea7c58dc |
completed | March 12, 2026, 10:58 p.m. |
| NER | Named-entity recognition | batch_69b350d2af088190ad7cb035d6e0f8c2 |
completed | March 12, 2026, 11:48 p.m. |
| PD | Predicate disambiguation | batch_69b34f4a07b08190a06ada0d9cbb14fb |
completed | March 12, 2026, 11:42 p.m. |
| PDg | Predicate description generation | batch_69b35034cd248190bae09e9d090e13ec |
completed | March 12, 2026, 11:45 p.m. |
Created at: March 12, 2026, 11:11 p.m.