Triple
T21388236
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Onkaparinga River |
E527565
|
entity |
| Predicate | nearbySuburb |
P41355
|
FINISHED |
| Object | Clarendon |
E1042325
|
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: Clarendon | Statement: [Onkaparinga River, nearbySuburb, Clarendon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Clarendon Context triple: [Onkaparinga River, nearbySuburb, Clarendon]
-
A.
Clarendon
Clarendon is a historic royal manor in Wiltshire, England, known for its medieval palace where several important legal and political reforms were issued.
-
B.
Clarendon
Clarendon is a vibrant urban neighborhood in Arlington, Virginia, known for its lively dining, shopping, and nightlife scene.
-
C.
Clarendon
Clarendon is a small town in Rutland County, Vermont, known for its rural character and scenic Green Mountain landscapes.
-
D.
Clarendon
chosen
Clarendon is a historic rural township in South Australia known for its vineyards, heritage buildings, and scenic location along the Onkaparinga River.
-
E.
Clarendon
Clarendon is a small city in the Texas Panhandle known as a regional hub for ranching and agriculture.
- 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_69e0b51ff3748190935c0a513c62a12b |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e8b0f6ce4c81909da915139610a2a7 |
completed | April 22, 2026, 11:28 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09b42a4fa88190a167206cedb206ab |
completed | May 17, 2026, 12:27 p.m. |
Created at: April 16, 2026, 5:12 p.m.