Triple
T19967373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Figueira da Horta |
E479972
|
entity |
| Predicate | hasCountryCode |
P189
|
FINISHED |
| Object | CV |
E1325323
|
NE 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: CV | Statement: [Figueira da Horta, hasCountryCode, CV]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: CV Context triple: [Figueira da Horta, hasCountryCode, CV]
-
A.
CV
CV is a common abbreviation for Chula Vista, a coastal city in Southern California located just south of San Diego.
-
B.
CV
CV is the standard abbreviation for the Central Vermont Railway, a historic regional railroad that operated primarily in Vermont and neighboring areas.
-
C.
CV
chosen
CV is the ISO 3166-1 alpha-2 country code for Cape Verde, an island nation off the west coast of Africa.
-
D.
CV
CV is the post-nominal abbreviation used to denote recipients of the Cross of Valour, a high-level decoration for extraordinary bravery.
-
E.
C.V.
C.V. is the autobiographical section of Stephen King’s book "On Writing," in which he recounts key experiences from his life that shaped him as a writer.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69d8e523c19881909f9197037200dde6 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65bc5e41881908c1e8867820f1c0c |
completed | April 20, 2026, 5 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a07fdd12e4c81908488a7a829ee7bc2 |
completed | May 16, 2026, 5:17 a.m. |
Created at: April 10, 2026, 1:54 p.m.