Triple
T19396630
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wood Green |
E485206
|
entity |
| Predicate | historicCounty |
P1069
|
FINISHED |
| Object | Middlesex |
E258941
|
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: Middlesex | Statement: [Wood Green, historicCounty, Middlesex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Middlesex Context triple: [Wood Green, historicCounty, Middlesex]
-
A.
Middlesex
Middlesex is a small village located in the Stann Creek District of Belize.
-
B.
Middlesex
Middlesex is a small town located in Yates County in the Finger Lakes region of New York State.
-
C.
Middlesex
Middlesex is a census division in southwestern Ontario, Canada, that includes the city of London and surrounding rural municipalities.
-
D.
Middlesex
Middlesex is a small rural town in Washington County, central Vermont, known for its scenic landscape and proximity to the state capital, Montpelier.
-
E.
County of Middlesex
chosen
The County of Middlesex is a historic county in southeast England that once encompassed much of what is now Greater London and surrounding areas.
- 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_69d8e8d5162481909db12435d9535c1a |
completed | April 10, 2026, 12:11 p.m. |
| NER | Named-entity recognition | batch_69e62573f5788190a635b92121db2cf7 |
completed | April 20, 2026, 1:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a072b8dd70c8190870a8478a111b887 |
completed | May 15, 2026, 2:19 p.m. |
Created at: April 10, 2026, 1:36 p.m.