Triple
T1968018
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Safety Management Certificate |
E42732
|
entity |
| Predicate | documentLanguage |
P31857
|
FINISHED |
| Object | usually English or official language of flag State |
—
|
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: usually English or official language of flag State | Statement: [Safety Management Certificate, documentLanguage, usually English or official language of flag State]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: documentLanguage Context triple: [Safety Management Certificate, documentLanguage, usually English or official language of flag State]
-
A.
languageOfWritings
Indicates that a specified language is the one in which certain writings or written works are composed.
-
B.
languageOfRecords
Indicates the language in which the records are written or maintained.
-
C.
documentationType
Indicates the specific category or kind of documentation associated with or required for an entity or process.
-
D.
contentLanguage
chosen
Indicates the language in which the content is expressed or intended to be understood.
-
E.
documentNumber
Indicates the unique identifying number assigned to a specific document in the relationship.
- 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_69a88711151c8190940b2572095059d7 |
completed | March 4, 2026, 7:25 p.m. |
| NER | Named-entity recognition | batch_69abb3cf15048190a51f73ed85e1b958 |
completed | March 7, 2026, 5:12 a.m. |
| PD | Predicate disambiguation | batch_69abaff7d4a48190ab0d51aefb1c4e31 |
completed | March 7, 2026, 4:56 a.m. |
Created at: March 4, 2026, 7:36 p.m.