Triple
T3296145
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Reginald Dyer |
E69219
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Dyer |
E69219
|
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: Dyer | Statement: [Reginald Dyer, familyName, Dyer]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Dyer Context triple: [Reginald Dyer, familyName, Dyer]
-
A.
Dyer
chosen
Dyer is a surname most infamously associated with British officer Reginald Dyer, known for ordering the 1919 Jallianwala Bagh massacre in Amritsar, India.
-
B.
Dyer
Dyer is a small unincorporated community in rural western Nevada, known for its remote desert setting and agricultural surroundings.
-
C.
Dixon
Dixon is a small agricultural city in Northern California known historically for sheep ranching and its annual May Fair.
-
D.
Brewster
Brewster is a coastal town on Cape Cod in Massachusetts known for its scenic beaches, historic charm, and bayside conservation lands.
-
E.
Brewster
Brewster is an English occupational surname historically associated with brewing ale or beer.
- 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_69ad859e529c8190a404273f53cb487d |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb077c60c81909782be5202ce5a43 |
completed | March 8, 2026, 5:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b2e86e867c8190a1e5042a4d4d92cb |
completed | March 12, 2026, 4:23 p.m. |
Created at: March 8, 2026, 3:10 p.m.