Triple
T9916821
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Samuel Harsnett |
E185890
|
entity |
| Predicate | birthPlace |
P1
|
FINISHED |
| Object | Essex |
E30848
|
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: Essex | Statement: [Samuel Harsnett, birthPlace, Essex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Essex Context triple: [Samuel Harsnett, birthPlace, Essex]
-
A.
Essex
chosen
Essex is a county in the east of England, known for its mix of rural landscapes, historic towns, and proximity to London.
-
B.
Suffolk
Suffolk is a historic rural county in eastern England known for its coastal towns, medieval villages, and agricultural landscapes.
-
C.
Suffolk
Suffolk is an independent city in southeastern Virginia known for its large land area, historic downtown, and role as part of the Hampton Roads region.
-
D.
Sussex
Sussex is a traditional British dual-purpose chicken breed valued for both its meat and egg production.
-
E.
Sussex
Sussex is a historic county in South East England, known for its coastal resorts, rolling South Downs, and rich medieval and maritime heritage.
- 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_69ca829b45f481909040f7b99a1976ed |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cdb540195881908f25f7dde5c66a75 |
completed | April 2, 2026, 12:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d299b0ed588190ae5fe6e87db668c7 |
completed | April 5, 2026, 5:19 p.m. |
Created at: March 30, 2026, 8:42 p.m.