Triple
T4654908
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Avro |
E102384
|
entity |
| Predicate | serializationModel |
P28140
|
FINISHED |
| Object | binary |
—
|
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: binary | Statement: [Avro, serializationModel, binary]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: serializationModel Context triple: [Avro, serializationModel, binary]
-
A.
serialized
Indicates that one entity has been converted into a sequential, storable or transmittable data format representing its structure or state.
-
B.
originalSerialization
Indicates that one entity is the initial or source serialized form from which another serialized representation is derived.
-
C.
associatedModelGeneration
Indicates that one entity is responsible for creating, producing, or generating another related model or representation.
-
D.
encodingStructure
chosen
Indicates the structural scheme or format used to encode information or data.
-
E.
dataModel
Indicates a relationship where an entity defines, uses, or is structured according to a specific data model or schema.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd6317ba70819089145766d3462e57 |
completed | March 20, 2026, 3:09 p.m. |
| PD | Predicate disambiguation | batch_69bd62126b0c81909ba3f21b21e30d54 |
completed | March 20, 2026, 3:04 p.m. |
Created at: March 20, 2026, 1:14 p.m.