Triple
T11564441
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Irving Penn |
E274219
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object | Penn |
E15422
|
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: Penn | Statement: [Irving Penn, familyName, Penn]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penn Context triple: [Irving Penn, familyName, Penn]
-
A.
Penn
chosen
Penn is a private Ivy League research university in Philadelphia known for its strong programs in business, law, medicine, and the liberal arts.
-
B.
Penn
Penn is the stage and given name of Penn Jillette, the outspoken magician, comedian, and half of the famed duo Penn & Teller.
-
C.
Penn
Penn is a 2006 Tamil-language romantic comedy film directed by A. Venkatesh and produced by AVM Productions.
-
D.
Pennsylvania
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.
-
E.
Pensilvania
Pensilvania is a municipality and town located in the Caldas Department of Colombia, known for its coffee-growing economy and mountainous Andean landscape.
- 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_69d6aae5ac3c81908d2b0a3a665665b2 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d88dd321f88190a57ecaf079fbbc3f |
completed | April 10, 2026, 5:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69e713bcc0048190bec14ac4ab84d51d |
completed | April 21, 2026, 6:05 a.m. |
Created at: April 8, 2026, 9:37 p.m.