Triple
T950582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | John Lyon |
E20510
|
entity |
| Predicate | residence |
P75
|
FINISHED |
| Object | Middlesex |
E14852
|
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: [John Lyon, residence, Middlesex]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Middlesex Context triple: [John Lyon, residence, Middlesex]
-
A.
Middlesex, England
chosen
Middlesex, England is a historic county in southeast England that once encompassed much of what is now Greater London.
-
B.
Essex
Essex is a county in the east of England, known for its mix of rural landscapes, historic towns, and proximity to London.
-
C.
Middlesex County
Middlesex County is a populous county in central New Jersey known for its diverse suburban communities, major transportation hubs, and proximity to New York City.
-
D.
Suffolk
Suffolk is a historic rural county in eastern England known for its coastal towns, medieval villages, and agricultural landscapes.
-
E.
Berkshire
Berkshire is a historic county in South East England known for its royal connections, including Windsor Castle, and its mix of affluent towns and rural landscapes.
- 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_69a493b0f2fc81908cd227480a5356a1 |
completed | March 1, 2026, 7:29 p.m. |
| NER | Named-entity recognition | batch_69a4b3d62e408190855b2883407f6c6b |
completed | March 1, 2026, 9:47 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac599117d48190b329ec50b9a632fc |
completed | March 7, 2026, 5 p.m. |
Created at: March 1, 2026, 7:40 p.m.