Triple
T14450076
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | G. G. Berry |
E358306
|
entity |
| Predicate | hasFamilyName |
P18
|
FINISHED |
| Object | Berry |
E325598
|
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: Berry | Statement: [G. G. Berry, hasFamilyName, Berry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Berry Context triple: [G. G. Berry, hasFamilyName, Berry]
-
A.
Berry
Berry is a historic province in central France known for its rural landscapes, medieval heritage, and traditional French culture.
-
B.
Berry
chosen
Berry is a common English surname borne by numerous individuals across various fields, including sports, politics, and the arts.
-
C.
Berry
Berry is a small historic town on the South Coast of New South Wales, Australia, known for its rural charm, heritage buildings, and popular weekend tourism.
-
D.
Pear
Pear is a sweet, bell-shaped fruit from the Pyrus genus, widely cultivated and eaten fresh, canned, or as juice around the world.
-
E.
Blueberry
Blueberry is a translucent light-blue color variant famously used on Apple’s early iMac G3 computers.
- 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_69d82794dfa081909b9134ad2e32244b |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de916139888190be219678e29a2a3a |
completed | April 14, 2026, 7:11 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5be12da481909b11290965ec48da |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:19 a.m.