Triple
T13338337
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Payment on Demand |
E317756
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object | Walter Hampden |
E486467
|
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: Walter Hampden | Statement: [Payment on Demand, starring, Walter Hampden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Walter Hampden Context triple: [Payment on Demand, starring, Walter Hampden]
-
A.
Walter Hampden
chosen
Walter Hampden was an American stage and film actor renowned for his distinguished Shakespearean performances and commanding presence in classical theatre.
-
B.
Harold Reid
Harold Reid was an American bass singer best known as a founding member of the country and gospel vocal group The Statler Brothers.
-
C.
John Hampson
John Hampson was the husband of renowned German actress Therese Giehse.
-
D.
Walter Hendricks
Walter Hendricks was an American educator best known as the founder and first president of Marlboro College in Vermont.
-
E.
Alfred Gilks
Alfred Gilks was an American cinematographer best known for his work on classic Hollywood films, including the musical "An American in Paris."
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99d01bf8481908cd3a99e5557b972 |
completed | April 11, 2026, 12:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a83125d481908fe02cf85651a7bb |
completed | May 3, 2026, 7:55 p.m. |
Created at: April 9, 2026, 9:31 p.m.