Triple
T9313165
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G-root |
E224052
|
entity |
| Predicate | hasRedundancyModel |
P51510
|
FINISHED |
| Object | anycast distributed instances |
—
|
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: anycast distributed instances | Statement: [G-root, hasRedundancyModel, anycast distributed instances]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasRedundancyModel Context triple: [G-root, hasRedundancyModel, anycast distributed instances]
-
A.
supportsRedundancy
chosen
Indicates that one entity provides or enables backup or failover capabilities for another to ensure continued operation if a primary component fails.
-
B.
hasReduplication
Indicates that an element involves repetition of a segment, syllable, or word (in whole or in part) as a systematic pattern.
-
C.
hasRealModel
Indicates that an abstract, theoretical, or simplified entity is associated with a corresponding concrete or physically instantiated model in the real world.
-
D.
hasConservationModel
Indicates that an entity is associated with or governed by a specific conservation model that describes how some quantity or property is preserved or transformed.
-
E.
hasCareModel
Indicates that one entity uses, follows, or is governed by a particular model or approach to providing care.
- 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_69ca8425f4fc81909c1c586e9a5b7530 |
completed | March 30, 2026, 2:09 p.m. |
| NER | Named-entity recognition | batch_69cd20b048a081909fd7ec0b6b863063 |
completed | April 1, 2026, 1:42 p.m. |
| PD | Predicate disambiguation | batch_69cc7a61e9a4819096eb014f3791ef2e |
completed | April 1, 2026, 1:52 a.m. |
Created at: March 30, 2026, 7:37 p.m.