Triple
T3237421
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MRT |
E67887
|
entity |
| Predicate | associatedCountryISONumericCode |
P27637
|
FINISHED |
| Object | 478 |
—
|
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: 478 | Statement: [MRT, associatedCountryISONumericCode, 478]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: associatedCountryISONumericCode Context triple: [MRT, associatedCountryISONumericCode, 478]
-
A.
associatedCountryCode
Indicates that there is a relationship linking something to the country identified by the given country code.
-
B.
associatedCountry
Indicates that there is a relevant connection or linkage between an entity and a specific country, such as origin, operation, or affiliation.
-
C.
hasISO3166-1NumericCode
chosen
Indicates that an entity is associated with a specific ISO 3166-1 numeric country code.
-
D.
associatedCountryMostProminently
Indicates the country with which an entity is most strongly or prominently associated, relative to any other countries it may be linked to.
-
E.
FIPSCountryCode
Indicates that an entity is associated with a specific country as identified by its FIPS (Federal Information Processing Standards) country code.
- 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_69ad858d27348190abb61c280b4c86a9 |
completed | March 8, 2026, 2:19 p.m. |
| NER | Named-entity recognition | batch_69adaef29bf48190a9aa3a39f0138428 |
completed | March 8, 2026, 5:16 p.m. |
| PD | Predicate disambiguation | batch_69ada4159e0481908cbbdd750f5e08c7 |
completed | March 8, 2026, 4:30 p.m. |
Created at: March 8, 2026, 3:08 p.m.