Triple
T18007583
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hatfield railway station |
E430793
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Hatfield |
—
|
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: Hatfield | Statement: [Hatfield railway station, locatedIn, Hatfield]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hatfield Context triple: [Hatfield railway station, locatedIn, Hatfield]
-
A.
Hatfield
Hatfield is a surname most prominently associated with Mark O. Hatfield, a long-serving U.S. senator and governor from Oregon.
-
B.
Hatfield
chosen
Hatfield is a historic town in Hertfordshire, England, known for Hatfield House and its strong connections to Tudor and Stuart royal history.
-
C.
Hatfield
Hatfield is a former coal mining town in South Yorkshire, England, historically associated with the Yorkshire coalfield.
-
D.
Harriton
Harriton is a surname most notably associated with American musician and songwriter Lisa Harriton.
-
E.
Carlisle
Carlisle is a historic borough in south-central Pennsylvania known for its military education institutions, colonial heritage, and role in the American Revolutionary era.
- 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_69d8b904530081908bf341d842464856 |
completed | April 10, 2026, 8:47 a.m. |
| NER | Named-entity recognition | batch_69e4b51d44088190bfcd35e532a4c02a |
completed | April 19, 2026, 10:57 a.m. |
Created at: April 10, 2026, 10:24 a.m.