Triple
T11911809
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bronte, Catania |
E283413
|
entity |
| Predicate | productSpecialty |
P90808
|
FINISHED |
| Object | Bronte pistachios |
—
|
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: Bronte pistachios | Statement: [Bronte, Catania, productSpecialty, Bronte pistachios]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: productSpecialty Context triple: [Bronte, Catania, productSpecialty, Bronte pistachios]
-
A.
shopSpecialty
chosen
Indicates that a shop primarily focuses on or is specially known for offering a particular type of product or service.
-
B.
craftSpecialty
Indicates that an entity has a particular area of specialized skill or focus within a craft or artisanal practice.
-
C.
producerCharacteristic
Indicates a relationship where a producer is associated with a particular attribute, quality, or trait that characterizes them or their production.
-
D.
styleSpecialty
Indicates a relationship where an entity’s expertise, focus, or specialization is in a particular style or stylistic approach.
-
E.
marketSpecialization
Indicates a relationship where an entity focuses its activities, products, or services on serving a specific segment or niche of a broader market.
- 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_69d6ab2c07e88190ba13b0d21fd6cf33 |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d8e528f6748190ac873a040a61fa93 |
completed | April 10, 2026, 11:55 a.m. |
| PD | Predicate disambiguation | batch_69d8bb3632ac8190b13e53c2b5db7125 |
completed | April 10, 2026, 8:56 a.m. |
Created at: April 8, 2026, 9:44 p.m.