Triple
T22168144
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SVCN |
E547849
|
entity |
| Predicate | hasICAOCode |
P419
|
FINISHED |
| Object | SVCN |
E547849
|
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: SVCN | Statement: [SVCN, hasICAOCode, SVCN]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: SVCN Context triple: [SVCN, hasICAOCode, SVCN]
-
A.
SVCN
chosen
SVCN is the ICAO airport code for Canaima airstrip, a small airport serving the Canaima National Park region in Venezuela.
-
B.
SVC
SVC is the commonly used abbreviation for Sri Venkateswara Creations, a prominent Indian film production company known for producing Telugu-language movies.
-
C.
SVC
SVC is a major vein in the upper chest that returns deoxygenated blood from the head, neck, upper limbs, and upper torso to the right atrium of the heart.
-
D.
SVC
SVC is scikit-learn’s implementation of a Support Vector Machine classifier used for supervised learning tasks such as binary and multiclass classification.
-
E.
VCN
VCN is Oracle Cloud Infrastructure’s software-defined private network environment that lets you securely isolate, segment, and control cloud resources.
- 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_69e11e3c4c5c81908d336165816b12e0 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a6642b08190980fa0c0d2bb4229 |
completed | April 28, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0aa606177081908b30cff5514cd8c7 |
completed | May 18, 2026, 5:39 a.m. |
Created at: April 16, 2026, 8:34 p.m.