Triple
T21476514
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St Fergus |
E529874
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object | Mintlaw |
—
|
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: Mintlaw | Statement: [St Fergus, hasNearbySettlement, Mintlaw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mintlaw Context triple: [St Fergus, hasNearbySettlement, Mintlaw]
-
A.
Mintlaw
chosen
Mintlaw is a village in Aberdeenshire, Scotland, known as a local service and administrative centre for the surrounding rural area.
-
B.
Minty
Minty is the childhood nickname of Harriet Tubman, the famed American abolitionist and Underground Railroad conductor who helped enslaved people escape to freedom.
-
C.
Mint
Mint is a popular personal finance management service and app that helps users track spending, budgets, and financial accounts in one place.
-
D.
Mint
Mint is an Indian business and financial daily newspaper known for its in-depth coverage of markets, economy, and corporate affairs.
-
E.
Greenlaw
Greenlaw is a small historic town in the Scottish Borders that once served as the county town of Berwickshire.
- 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_69e0c459acb481909bb6ee452a0045c7 |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea187a3c8190b3f33bd760dc1f54 |
completed | April 23, 2026, 9:44 a.m. |
Created at: April 16, 2026, 6:20 p.m.