Triple
T10091616
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Paul Engle |
E215356
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Engle |
E357526
|
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: Engle | Statement: [Paul Engle, familyName, Engle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Engle Context triple: [Paul Engle, familyName, Engle]
-
A.
Engle
chosen
Engle is a surname most notably associated with Joe Engle, an American astronaut and test pilot.
-
B.
Engel
Engel is a German-origin surname borne by numerous notable individuals across fields such as science, politics, and the arts.
-
C.
Englehart
Englehart is a small town located in the Timiskaming District of northeastern Ontario, Canada.
-
D.
The Englert
The Englert is a historic performing arts venue in Iowa City, Iowa, hosting live music, theater, film, and community events.
-
E.
Okun
Okun is a surname most notably associated with figures such as economist Arthur Okun, known for Okun's law relating unemployment and economic output.
- 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_69ca83a1eed081908b2e9580f2ebeea7 |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cdd05aa02081908fba02e7085c6d7c |
completed | April 2, 2026, 2:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d2b6a587e0819093f38d5e69db43ec |
completed | April 5, 2026, 7:23 p.m. |
Created at: March 30, 2026, 9:01 p.m.