Triple
T7354290
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Honeymoon Machine |
E169583
|
entity |
| Predicate | playwright |
P12368
|
FINISHED |
| Object | Lorenzo Semple Jr. |
E104992
|
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: Lorenzo Semple Jr. | Statement: [The Honeymoon Machine, playwright, Lorenzo Semple Jr.]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lorenzo Semple Jr. Context triple: [The Honeymoon Machine, playwright, Lorenzo Semple Jr.]
-
A.
Lorenzo Semple Jr.
chosen
Lorenzo Semple Jr. was an American screenwriter best known for his work on the 1960s Batman TV series and several high-profile films, including thrillers and action-adventures of the 1970s.
-
B.
Daniel Lugo
Daniel Lugo is the ambitious, bodybuilding ringleader of the criminal scheme at the center of the dark comedy crime film "Pain & Gain."
-
C.
Roderic Dallas
Roderic Dallas was a distinguished Australian fighter ace of World War I who became one of the leading pilots in British naval aviation.
-
D.
Frank Dominguez
Frank Dominguez is an entrepreneur best known as a founder of the cloud-based software company Salesforce.
-
E.
Frank Dominguez
Frank Dominguez is a person whose specific public background or notable achievements are not clearly identifiable from the given information.
- 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_69c68a59f2288190877ca15c19b1e822 |
completed | March 27, 2026, 1:47 p.m. |
| NER | Named-entity recognition | batch_69c6f10e71fc81909307ca39a61142d3 |
completed | March 27, 2026, 9:05 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c7faa25960819084ecb6dbf9369ba5 |
completed | March 28, 2026, 3:58 p.m. |
Created at: March 27, 2026, 3:05 p.m.