Triple
T3516677
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | C. Robert Kehler |
E74323
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object | Pennsylvania |
E13698
|
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: Pennsylvania | Statement: [C. Robert Kehler, placeOfBirth, Pennsylvania]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Pennsylvania Context triple: [C. Robert Kehler, placeOfBirth, Pennsylvania]
-
A.
Pennsylvania
chosen
Pennsylvania is a historically significant U.S. state in the Mid-Atlantic and Northeastern regions, known for cities like Philadelphia and Pittsburgh and its central role in the nation’s founding.
-
B.
Pensilvania
Pensilvania is a municipality and town located in the Caldas Department of Colombia, known for its coffee-growing economy and mountainous Andean landscape.
-
C.
Pennsylvania and New Jersey
Pennsylvania and New Jersey are neighboring U.S. states in the Mid-Atlantic region, separated for much of their length by the Delaware River.
-
D.
Penn
Penn is the stage and given name of Penn Jillette, the outspoken magician, comedian, and half of the famed duo Penn & Teller.
-
E.
Penn
Penn is a private Ivy League research university in Philadelphia known for its strong programs in business, law, medicine, and the liberal arts.
- 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_69ad85cfb5c881909c9a2edd9d6043cc |
completed | March 8, 2026, 2:21 p.m. |
| NER | Named-entity recognition | batch_69adbc31c0688190a890621a901f5f5f |
completed | March 8, 2026, 6:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b37e7da9c08190ab417b45339513bd |
completed | March 13, 2026, 3:03 a.m. |
Created at: March 8, 2026, 3:19 p.m.