Triple
T20120728
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fun in Acapulco |
E490598
|
entity |
| Predicate | screenwriter |
P2831
|
FINISHED |
| Object | Allan Weiss |
—
|
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: Allan Weiss | Statement: [Fun in Acapulco, screenwriter, Allan Weiss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allan Weiss Context triple: [Fun in Acapulco, screenwriter, Allan Weiss]
-
A.
Allan Weiss
chosen
Allan Weiss was an American screenwriter best known for his work on mid-20th-century Hollywood films, including several Elvis Presley movies.
-
B.
Allan Weiss
Allan Weiss is a writer best known for his work on the book *A House in Hawaii*.
-
C.
Andrew Weiss
Andrew Weiss is a notable individual whose specific prominence or field of recognition is not clearly identifiable from the given information alone.
-
D.
Allan N. Weiss
Allan N. Weiss is an American entrepreneur and economist best known for collaborating with Robert J. Shiller on real estate market indices and housing-related financial innovations.
-
E.
Alan Weiss
Alan Weiss is a prominent American management consultant and author known for his influential work on consulting practices, value-based fees, and professional development for consultants.
- 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_69da62636cc08190982cc71733a17b8d |
completed | April 11, 2026, 3:01 p.m. |
| NER | Named-entity recognition | batch_69e6673e79dc81908fbd387c067fce79 |
completed | April 20, 2026, 5:49 p.m. |
Created at: April 11, 2026, 11:30 p.m.