Triple
T15970532
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lake Chivero |
E387307
|
entity |
| Predicate | nearbySettlement |
P350
|
FINISHED |
| Object | Norton |
E384587
|
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: Norton | Statement: [Lake Chivero, nearbySettlement, Norton]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Norton Context triple: [Lake Chivero, nearbySettlement, Norton]
-
A.
Norton
Norton is a surname of English origin borne by numerous notable individuals across fields such as literature, politics, and the arts.
-
B.
Norton
chosen
Norton is a town in Zimbabwe located near the Manyame River, known for its agricultural activities and proximity to the capital, Harare.
-
C.
Norton
Norton is a well-known cybersecurity and antivirus software brand that provides protection solutions for personal computers, mobile devices, and online activities.
-
D.
Norton
Norton is a town within the Teesside urban area in North East England, known for its historic high street and village green.
-
E.
Norton
Norton is a village in Gloucestershire, England, situated near the River Chelt and close to the town of Cheltenham.
- 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_69d86da94ccc819083d187f5dc6a123e |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e157291214819088d65e984609e42c |
completed | April 16, 2026, 9:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffbe88fa308190942d37cf67458396 |
completed | May 9, 2026, 11:08 p.m. |
Created at: April 10, 2026, 4:54 a.m.