Triple
T23376625
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harold John Ockenga |
E593623
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Ockenga |
E593623
|
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: Ockenga | Statement: [Harold John Ockenga, familyName, Ockenga]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ockenga Context triple: [Harold John Ockenga, familyName, Ockenga]
-
A.
Ockenga
chosen
Ockenga is a surname most notably associated with Harold Ockenga, a prominent American evangelical leader and theologian.
-
B.
Ozinga
Ozinga is a Dutch surname most notably associated with individuals such as Sjoukje Ozinga.
-
C.
Mackecknie
Mackecknie is the distinctive middle name of the fictional character Eulalie Mackecknie Shinn from Meredith Willson’s musical "The Music Man."
-
D.
Oberholzer
Oberholzer is a surname of Germanic origin, commonly found in German-speaking regions and among their diasporas.
-
E.
O'Steen
O'Steen is a surname most notably associated with American film editor Sam O'Steen, known for his work on several acclaimed Hollywood films.
- 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_69e25d268a50819095f2fd479da8ef3f |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a3b3cc348190953d0b0ebac9c5dd |
completed | April 29, 2026, 6:22 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c5de1c1748190994c4a95d0592fef |
completed | May 19, 2026, 12:56 p.m. |
Created at: April 17, 2026, 5:33 p.m.