Triple
T19209868
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eugene M. Lang |
E480327
|
entity |
| Predicate | name |
P16
|
FINISHED |
| Object | Eugene M. Lang |
—
|
NE NERFINISHED |
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: Eugene M. Lang | Statement: [Eugene M. Lang, name, Eugene M. Lang]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Eugene M. Lang Context triple: [Eugene M. Lang, name, Eugene M. Lang]
-
A.
Eugene M. Lang
chosen
Eugene M. Lang was an American businessman and philanthropist best known for his transformative support of education and entrepreneurship initiatives.
-
B.
Ernest F. Coe
Ernest F. Coe was an American landscape architect and conservationist best known as a leading advocate for the creation and protection of Everglades National Park.
-
C.
Carl Lerner
Carl Lerner was an American film editor best known for his work on influential films of the 1960s and 1970s, including the thriller "Klute."
-
D.
Philip J. Lang
Philip J. Lang was an American orchestrator best known for his work on numerous mid-20th-century Broadway musicals.
-
E.
Bernard M. Gordon
Bernard M. Gordon is an American engineer, inventor, and philanthropist known for pioneering work in high-speed analog-to-digital conversion and for major contributions to engineering education.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8e8cb8c348190b52075823911c869 |
completed | April 10, 2026, 12:10 p.m. |
| NER | Named-entity recognition | batch_69e5f9a0d1248190953e36e44f0cafdd |
completed | April 20, 2026, 10:02 a.m. |
Created at: April 10, 2026, 1:21 p.m.