Triple
T9836669
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Blonde Venus |
E239118
|
entity |
| Predicate | character |
P662
|
FINISHED |
| Object |
Nick Townsend
Nick Townsend is a central male character in the 1932 film "Blonde Venus," involved in the dramatic romantic and moral conflicts that drive the story.
|
E832778
|
NE FINISHED |
How this triple was built (4 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: Nick Townsend | Statement: [Blonde Venus, character, Nick Townsend]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Nick Townsend Context triple: [Blonde Venus, character, Nick Townsend]
-
A.
William Tummel
William Tummel was a Hollywood film assistant director who received an Academy Award for his work in the early years of the Oscars.
-
B.
Paul Sidwell
Paul Sidwell is a linguist specializing in the historical and comparative study of Austroasiatic languages, particularly known for his reconstruction work on the Palaungic branch.
-
C.
Jon Cornish
Jon Cornish is a former Canadian Football League star running back who became a prominent community leader and chancellor of the University of Calgary.
-
D.
Ben Shepherd
Ben Shepherd is an American musician best known as the longtime bassist for the influential grunge band Soundgarden.
-
E.
Ed Shearmur
Ed Shearmur is a British film composer known for scoring a wide range of Hollywood movies across genres.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Nick Townsend Triple: [Blonde Venus, character, Nick Townsend]
Generated description
Nick Townsend is a central male character in the 1932 film "Blonde Venus," involved in the dramatic romantic and moral conflicts that drive the story.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Nick Townsend Target entity description: Nick Townsend is a central male character in the 1932 film "Blonde Venus," involved in the dramatic romantic and moral conflicts that drive the story.
-
A.
William Tummel
William Tummel was a Hollywood film assistant director who received an Academy Award for his work in the early years of the Oscars.
-
B.
Paul Sidwell
Paul Sidwell is a linguist specializing in the historical and comparative study of Austroasiatic languages, particularly known for his reconstruction work on the Palaungic branch.
-
C.
Jon Cornish
Jon Cornish is a former Canadian Football League star running back who became a prominent community leader and chancellor of the University of Calgary.
-
D.
Ben Shepherd
Ben Shepherd is an American musician best known as the longtime bassist for the influential grunge band Soundgarden.
-
E.
Ed Shearmur
Ed Shearmur is a British film composer known for scoring a wide range of Hollywood movies across genres.
- F. None of above. chosen
Provenance (5 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_69ca84e314108190978324a4bdb959f8 |
completed | March 30, 2026, 2:12 p.m. |
| NER | Named-entity recognition | batch_69cdb33b07688190b78a70cf535c3efc |
completed | April 2, 2026, 12:07 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d23cfc50948190aae82dced585fa29 |
completed | April 5, 2026, 10:44 a.m. |
| NEDg | Description generation | batch_69d23eb1c1f481908404225dcccd0697 |
completed | April 5, 2026, 10:51 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69d242aea6a08190a73a836e59865c35 |
completed | April 5, 2026, 11:08 a.m. |
Created at: March 30, 2026, 8:33 p.m.