Triple
T23065092
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hans Albers |
E575014
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object |
Gold
Gold is a 1934 German science fiction film starring Hans Albers that centers on a scientist’s attempt to create gold through nuclear transmutation.
|
E1568672
|
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: Gold | Statement: [Hans Albers, notableWork, Gold]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Gold Context triple: [Hans Albers, notableWork, Gold]
-
A.
Gold
Gold was the codename for one of the five Allied landing beaches used by British forces during the D-Day invasion of Normandy in World War II.
-
B.
Gold
Gold is a chemical element and precious metal highly valued for its rarity, luster, and use in jewelry, currency, and electronics.
-
C.
Gold
Gold is a 2016 American crime adventure film in which Matthew McConaughey stars as a prospector chasing a potentially fraudulent gold discovery in the Indonesian jungle.
-
D.
Gold
"Gold" is a popular song performed by British singer Tony Hadley as the lead vocalist of the band Spandau Ballet, known for its anthemic style and enduring 1980s appeal.
-
E.
Gold
"Gold" is a satirical book by John Stewart that blends sharp humor with social and political commentary.
- 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: Gold Triple: [Hans Albers, notableWork, Gold]
Generated description
Gold is a 1934 German science fiction film starring Hans Albers that centers on a scientist’s attempt to create gold through nuclear transmutation.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Gold Target entity description: Gold is a 1934 German science fiction film starring Hans Albers that centers on a scientist’s attempt to create gold through nuclear transmutation.
-
A.
Gold
Gold is a 2016 American crime adventure film in which Matthew McConaughey stars as a prospector chasing a potentially fraudulent gold discovery in the Indonesian jungle.
-
B.
Gold
Gold is a chemical element and precious metal highly valued for its rarity, luster, and use in jewelry, currency, and electronics.
-
C.
Gold
Gold was the codename for one of the five Allied landing beaches used by British forces during the D-Day invasion of Normandy in World War II.
-
D.
Gold
"Gold" is a popular song performed by British singer Tony Hadley as the lead vocalist of the band Spandau Ballet, known for its anthemic style and enduring 1980s appeal.
-
E.
Gold
"Gold" is a satirical book by John Stewart that blends sharp humor with social and political commentary.
- 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_69e245bd6e4c8190bb8942245b68cad5 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f189a2eb5c81908a90e22ff2a56430 |
completed | April 29, 2026, 4:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0c0aeae0d08190b2f75cd2b44a0403 |
completed | May 19, 2026, 7:02 a.m. |
| NEDg | Description generation | batch_6a0c0c69b46481908026d0d065e3c926 |
completed | May 19, 2026, 7:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0c10498e2c8190ad4eb736a86e796e |
completed | May 19, 2026, 7:24 a.m. |
Created at: April 17, 2026, 3:55 p.m.