Triple
T12530803
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hyman Arluck |
E299556
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Anya Taranda
Anya Taranda was an American fashion model and actress best known for her work in the 1930s and 1940s.
|
E988392
|
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: Anya Taranda | Statement: [Hyman Arluck, spouse, Anya Taranda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anya Taranda Context triple: [Hyman Arluck, spouse, Anya Taranda]
-
A.
Anya Oliwa
Anya Oliwa is a key resistance member and ally of protagonist B.J. Blazkowicz in the modern Wolfenstein video games, known for her intelligence, bravery, and crucial role in the fight against the Nazi regime.
-
B.
Anya Richt
Anya Richt is the daughter of former college football head coach Mark Richt.
-
C.
Anya Thorensen
Anya Thorensen is a character in the science fiction horror film "Annihilation," known as one of the expedition members who ventures into the mysterious and dangerous area called the Shimmer.
-
D.
Anya Derevkova
Anya Derevkova is a Marvel Comics character trained as an elite assassin through the Soviet Black Widow program.
-
E.
Anya Major
Anya Major is a British athlete and actress best known for playing the hammer-throwing heroine in Apple’s iconic 1984 Macintosh television commercial.
- 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: Anya Taranda Triple: [Hyman Arluck, spouse, Anya Taranda]
Generated description
Anya Taranda was an American fashion model and actress best known for her work in the 1930s and 1940s.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anya Taranda Target entity description: Anya Taranda was an American fashion model and actress best known for her work in the 1930s and 1940s.
-
A.
Anya Oliwa
Anya Oliwa is a key resistance member and ally of protagonist B.J. Blazkowicz in the modern Wolfenstein video games, known for her intelligence, bravery, and crucial role in the fight against the Nazi regime.
-
B.
Anya Richt
Anya Richt is the daughter of former college football head coach Mark Richt.
-
C.
Anya Thorensen
Anya Thorensen is a character in the science fiction horror film "Annihilation," known as one of the expedition members who ventures into the mysterious and dangerous area called the Shimmer.
-
D.
Anya Derevkova
Anya Derevkova is a Marvel Comics character trained as an elite assassin through the Soviet Black Widow program.
-
E.
Anya Major
Anya Major is a British athlete and actress best known for playing the hammer-throwing heroine in Apple’s iconic 1984 Macintosh television commercial.
- 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_69d6ada5cdd48190860d9ce30aff69be |
completed | April 8, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69d95469d100819087c83bc55e3ec9ce |
completed | April 10, 2026, 7:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f64bc8866c81908821525b595715e2 |
completed | May 2, 2026, 7:08 p.m. |
| NEDg | Description generation | batch_69f64da25bf88190889273bf41e2f154 |
completed | May 2, 2026, 7:16 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f65213ed84819086fda178aaf9e774 |
completed | May 2, 2026, 7:35 p.m. |
Created at: April 8, 2026, 9:57 p.m.