Triple
T5831456
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sarah Thorne |
E129357
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Thorne |
E244848
|
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: Thorne | Statement: [Sarah Thorne, familyName, Thorne]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Thorne Context triple: [Sarah Thorne, familyName, Thorne]
-
A.
Thorne
chosen
Thorne is a surname most prominently associated with Kip Thorne, the Nobel Prize–winning theoretical physicist known for his work on gravitation and black holes.
-
B.
Thorne
Thorne is a small market town in South Yorkshire, England, known for its historic waterways, including canals and navigable rivers.
-
C.
Thorney
Thorney is a village in Cambridgeshire, England, historically centered around its important medieval Benedictine abbey.
-
D.
Taffs Well
Taffs Well is a village in South Wales known for its historic thermal spring and proximity to Cardiff at the southern end of the Taff Valley.
-
E.
Thornbury
Thornbury is a historic market town in South Gloucestershire, England, known for its medieval castle and traditional high street.
- 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_69c00849d55481908b4f9f5543e0bf6d |
completed | March 22, 2026, 3:18 p.m. |
| NER | Named-entity recognition | batch_69c0346ac31c8190bbd28444f75da875 |
completed | March 22, 2026, 6:26 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0a19010008190af5600a035246c1a |
completed | March 23, 2026, 2:12 a.m. |
Created at: March 22, 2026, 3:54 p.m.