Triple
T8748352
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | IN-MZ |
E207889
|
entity |
| Predicate | languageOfSubdivisionCodePart |
P50512
|
FINISHED |
| Object | Latin script |
—
|
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: Latin script | Statement: [IN-MZ, languageOfSubdivisionCodePart, Latin script]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: languageOfSubdivisionCodePart Context triple: [IN-MZ, languageOfSubdivisionCodePart, Latin script]
-
A.
subdivisionNameLanguage
Indicates the language in which the name of a subdivision (such as a region, district, or administrative unit) is expressed.
-
B.
associatedSubdivisionISO3166-1Alpha2
Indicates that a subdivision (such as a state or province) is associated with a specific country identified by its ISO 3166-1 alpha-2 code.
-
C.
subdivisionISONameLanguage
chosen
Indicates the language in which the ISO-standardized name of a geographic or administrative subdivision is expressed.
-
D.
hasSubdivisionCodePart
Indicates that an entity’s subdivision code includes or is composed of the referenced code segment or component.
-
E.
regionLanguage
Indicates that a particular language is used or officially recognized within a specific geographic region.
- 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_69ca835bb2bc819084bb5906cb6ef7f8 |
completed | March 30, 2026, 2:06 p.m. |
| NER | Named-entity recognition | batch_69cc5da2d67c819094b2b39c78384d0d |
completed | March 31, 2026, 11:49 p.m. |
| PD | Predicate disambiguation | batch_69cc5c160dac8190b4aeb4bf0529de52 |
completed | March 31, 2026, 11:43 p.m. |
Created at: March 30, 2026, 6:39 p.m.