Triple
T25253967
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | African Union passport |
E633116
|
entity |
| Predicate | documentCategory |
P19006
|
FINISHED |
| Object | international identity document |
—
|
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: international identity document | Statement: [African Union passport, documentCategory, international identity document]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: documentCategory Context triple: [African Union passport, documentCategory, international identity document]
-
A.
documentationType
chosen
Indicates the specific category or kind of documentation associated with or required for an entity or process.
-
B.
documentsTypeOfWork
Indicates that one entity records or specifies the category or type of work associated with another entity.
-
C.
documentFamily
Indicates a relationship where one document is categorized as belonging to the same family or group as another document, typically based on shared origin, content, or classification.
-
D.
documentsCommunity
Indicates that an entity records, describes, or provides information about a particular community.
-
E.
officeCategory
Indicates the classification or type of an office within a defined categorization scheme.
- 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_69e75a922ad481908f4f1f884583cb42 |
completed | April 21, 2026, 11:08 a.m. |
| NER | Named-entity recognition | batch_69f48b9b687881908fd87a2f5fa0b1e7 |
completed | May 1, 2026, 11:16 a.m. |
| PD | Predicate disambiguation | batch_69f4806d93dc8190b9dff4c63186faff |
completed | May 1, 2026, 10:29 a.m. |
Created at: April 21, 2026, 1:12 p.m.