Triple
T13968882
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Binyamina |
E335999
|
entity |
| Predicate | region |
P40
|
FINISHED |
| Object | Sharon plain |
E484960
|
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: Sharon plain | Statement: [Binyamina, region, Sharon plain]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Sharon plain Context triple: [Binyamina, region, Sharon plain]
-
A.
Sharon plain
chosen
Sharon plain is a fertile coastal region in central Israel known historically for its agriculture and ancient settlements.
-
B.
New Sharon
New Sharon is a small rural city located in central Iowa, United States.
-
C.
Sharon Heights
Sharon Heights is an affluent residential neighborhood in Menlo Park, California, known for its upscale homes, golf course, and proximity to Silicon Valley.
-
D.
Sholden
Sholden is a small village and civil parish in Kent, England, situated near the coastal town of Deal.
-
E.
Jerrys Plains
Jerrys Plains is a small rural village in New South Wales, Australia, known for its agricultural lands, horse studs, and proximity to the Hunter Valley wine region.
- 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_69d81c61f3508190aaf2ca0dc0002c59 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de2e8daeac8190aadd4b3b60222482 |
completed | April 14, 2026, 12:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fbac90250881908f1945793d261752 |
completed | May 6, 2026, 9:03 p.m. |
Created at: April 9, 2026, 10:18 p.m.