Triple
T8023471
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Chew Stoke |
E186794
|
entity |
| Predicate | locatedNear |
P294
|
FINISHED |
| Object | Chew Magna |
E186793
|
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: Chew Magna | Statement: [Chew Stoke, locatedNear, Chew Magna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Chew Magna Context triple: [Chew Stoke, locatedNear, Chew Magna]
-
A.
Chew Magna
chosen
Chew Magna is a historic village and civil parish in Somerset, England, known for its medieval church, traditional architecture, and location near Chew Valley Lake.
-
B.
Baiju
Baiju was a 13th-century Mongol general who led Mongol forces in their campaigns into Eastern Europe.
-
C.
Tobo
Tobo is a small locality in eastern Sweden situated within Tierp Municipality in Uppsala County.
-
D.
Groot
Groot is a sentient, tree-like alien superhero from Marvel Comics and the Marvel Cinematic Universe, known for his limited vocabulary and close partnership with Rocket Raccoon.
-
E.
Banzi
Banzi is a town in the Basilicata region of southern Italy, known as the modern site near the ancient Lucanian city of Bantia.
- 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_69ca82ad4e2c8190a693e3c9e30fe66f |
completed | March 30, 2026, 2:03 p.m. |
| NER | Named-entity recognition | batch_69cb3e8fb6788190a16413051ec26988 |
completed | March 31, 2026, 3:25 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cc56d41ec08190a19cb28e2e4b5bfe |
completed | March 31, 2026, 11:20 p.m. |
Created at: March 30, 2026, 5:21 p.m.