Triple
T9682322
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reginald Gardiner |
E234313
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Gardiner |
E158953
|
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: Gardiner | Statement: [Reginald Gardiner, familyName, Gardiner]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gardiner Context triple: [Reginald Gardiner, familyName, Gardiner]
-
A.
Gardiner
chosen
Gardiner is an English surname historically associated with Princess Muna al-Hussein, the British-born mother of King Abdullah II of Jordan.
-
B.
Gardiner
Gardiner is a residential suburb in Melbourne, Victoria, known for its access to public transport and proximity to the city’s inner east.
-
C.
Gardiner
Gardiner is a commonly used short name for the Gardiner Expressway, a major elevated highway running along Toronto’s waterfront.
-
D.
Orono
Orono is a suburban city in Minnesota known for its affluent residential communities and scenic location along the north shore of Lake Minnetonka.
-
E.
Orono
Orono is a small rural village in Ontario, Canada, known for its historic downtown, agricultural surroundings, and community events.
- 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_69ca84c99e34819092e5563a7106cfca |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cd9ccdbcc48190a4a9a70b3f419ac2 |
completed | April 1, 2026, 10:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d19f736b988190b963e216a805316d |
completed | April 4, 2026, 11:32 p.m. |
Created at: March 30, 2026, 8:16 p.m.