Triple
T15063582
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monster-in-Law |
E379698
|
entity |
| Predicate | writer |
P1360
|
FINISHED |
| Object |
Anya Kochoff
Anya Kochoff is a screenwriter best known for writing the romantic comedy film "Monster-in-Law."
|
E1135042
|
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 Kochoff | Statement: [Monster-in-Law, writer, Anya Kochoff]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anya Kochoff Context triple: [Monster-in-Law, writer, Anya Kochoff]
-
A.
Anya Taranda
Anya Taranda was an American fashion model and actress best known for her work in the 1930s and 1940s.
-
B.
Anya Richt
Anya Richt is the daughter of former college football head coach Mark Richt.
-
C.
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.
-
D.
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.
-
E.
Anya Derevkova
Anya Derevkova is a Marvel Comics character trained as an elite assassin through the Soviet Black Widow program.
- 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 Kochoff Triple: [Monster-in-Law, writer, Anya Kochoff]
Generated description
Anya Kochoff is a screenwriter best known for writing the romantic comedy film "Monster-in-Law."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Anya Kochoff Target entity description: Anya Kochoff is a screenwriter best known for writing the romantic comedy film "Monster-in-Law."
-
A.
Anya Taranda
Anya Taranda was an American fashion model and actress best known for her work in the 1930s and 1940s.
-
B.
Anya Richt
Anya Richt is the daughter of former college football head coach Mark Richt.
-
C.
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.
-
D.
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.
-
E.
Anya Derevkova
Anya Derevkova is a Marvel Comics character trained as an elite assassin through the Soviet Black Widow program.
- 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_69d85cd7683881908d405c1b5d7b4f7f |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69dedee803ac81908bb7d66e49c2eb72 |
completed | April 15, 2026, 12:42 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fea5c8b3ac8190b8fc921b6e6eeed5 |
completed | May 9, 2026, 3:11 a.m. |
| NEDg | Description generation | batch_69fea74447d481908b290d6be0f0898e |
completed | May 9, 2026, 3:17 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fea81b77708190b0aafcb504dc72d1 |
completed | May 9, 2026, 3:20 a.m. |
Created at: April 10, 2026, 3:02 a.m.