Triple
T14418874
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Joe Engle |
E357526
|
entity |
| Predicate | hasSurname |
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: [Joe Engle, hasSurname, Engle]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Engle Context triple: [Joe Engle, hasSurname, 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.
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.
-
E.
Engen
Engen is a surname of Norwegian origin borne by various notable individuals across fields such as aviation, sports, and public service.
- 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_69d82793421c8190861eb0e673b085de |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de910eb354819089d5d5a46919eb49 |
completed | April 14, 2026, 7:10 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd5bc961c48190b67dceb2f07977fd |
completed | May 8, 2026, 3:43 a.m. |
Created at: April 10, 2026, 1:18 a.m.