Triple
T191218
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | La Habana Province |
E3724
|
entity |
| Predicate | hadISOCode |
P189
|
FINISHED |
| Object | CU-02 |
—
|
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: CU-02 | Statement: [La Habana Province, hadISOCode, CU-02]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hadISOCode Context triple: [La Habana Province, hadISOCode, CU-02]
-
A.
hasISOCode
chosen
Indicates that an entity is associated with a specific standardized ISO code that uniquely identifies it according to ISO conventions.
-
B.
hasCountry
Indicates that one entity possesses, is associated with, or is located within a specific country.
-
C.
ISOCode
Indicates that an entity is associated with a specific standardized code defined by the International Organization for Standardization (ISO).
-
D.
hasRegionCode
Indicates that an entity is associated with a specific regional identifier or code.
-
E.
hasIATAcode
Indicates that an entity, typically a transportation facility like an airport, is associated with a specific IATA (International Air Transport Association) 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_69a2548debd48190ae3a06d6e65b53c6 |
completed | Feb. 28, 2026, 2:35 a.m. |
| NER | Named-entity recognition | batch_69a25964fc5c8190bd3e37daaf695ecf |
completed | Feb. 28, 2026, 2:56 a.m. |
| PD | Predicate disambiguation | batch_69a25673ce3c8190b1a3df5b814a0595 |
completed | Feb. 28, 2026, 2:44 a.m. |
Created at: Feb. 28, 2026, 2:41 a.m.