Triple
T1313948
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Health Education England |
E28057
|
entity |
| Predicate | headquartersLocation |
P62
|
FINISHED |
| Object | Leeds |
E22344
|
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: Leeds | Statement: [Health Education England, headquartersLocation, Leeds]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Leeds Context triple: [Health Education England, headquartersLocation, Leeds]
-
A.
Leeds
chosen
Leeds is a major city in West Yorkshire, England, known as a key commercial, cultural, and educational center in the north of the country.
-
B.
Sheffield
Sheffield is a small rural town in southwestern Massachusetts known for its scenic landscapes, historic charm, and location in the Berkshires.
-
C.
Sheffield
Sheffield is a major industrial city in South Yorkshire, England, historically renowned for its steel production and role in the Industrial Revolution.
-
D.
Sheffield
Sheffield is a small city in northwestern Alabama, part of the Muscle Shoals metropolitan area known for its rich musical heritage and Tennessee River location.
-
E.
Leeds city region
The Leeds city region is a major economic hub in West Yorkshire, England, encompassing Leeds and its surrounding towns and cities with strong links to finance, services, and manufacturing.
- 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_69a498532c3481909223b74af2e578df |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1585d3081909ca0221c23de63ba |
completed | March 1, 2026, 10:44 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adc97fe2808190b421329ed239af6f |
completed | March 8, 2026, 7:09 p.m. |
Created at: March 1, 2026, 7:55 p.m.