Triple
T20658489
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | South Somerset |
E507690
|
entity |
| Predicate | containsVillage |
P4011
|
FINISHED |
| Object | Merriott |
—
|
NE NERFINISHED |
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: Merriott | Statement: [South Somerset, containsVillage, Merriott]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Merriott Context triple: [South Somerset, containsVillage, Merriott]
-
A.
Merriott
chosen
Merriott is a rural village in South Somerset, England, known for its historic buildings and agricultural surroundings.
-
B.
Fairmont
Fairmont is a global luxury hotel brand known for its historic landmark properties and upscale accommodations.
-
C.
Fairmont
Fairmont is a small unincorporated community and census-designated place located in Will County, Illinois, United States.
-
D.
Fairmont
Fairmont is a city in north-central West Virginia known historically for its role in the coal industry and as part of the greater Morgantown metropolitan area.
-
E.
Grant Hotel
Grant Hotel is a historic luxury hotel in downtown San Diego, California, known for its early 20th-century architecture and longstanding role as a city landmark.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b4bf58c081908e52a4500e03ff83 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6b2eefd5c8190a71d4be690a6ae0e |
completed | April 20, 2026, 11:12 p.m. |
Created at: April 16, 2026, 11:43 a.m.