Triple
T13338339
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Payment on Demand |
E317756
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Katherine Warren
Katherine Warren was an American character actress known for her supporting roles in mid-20th-century films and television.
|
E1058810
|
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: Katherine Warren | Statement: [Payment on Demand, starring, Katherine Warren]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Katherine Warren Context triple: [Payment on Demand, starring, Katherine Warren]
-
A.
Katherine Green
Katherine Green is a film editor known for her work on the romantic comedy "40 Days and 40 Nights."
-
B.
Katherine Lester
Katherine Lester is a central character in the period drama film "Lady Macbeth," known for her intense and morally complex journey portrayed by Florence Pugh.
-
C.
Katherine Swift
Katherine Swift is a notable individual recognized for her association with the Swift surname, likely distinguished in her professional or creative field.
-
D.
Catherine Faylen
Catherine Faylen is an American actress best known for her film and television roles in the 1940s and 1950s.
-
E.
Katherine Willis
Katherine Willis is an American actress known for her work in film and television, including a role in the action drama "Mercury Plains."
- 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: Katherine Warren Triple: [Payment on Demand, starring, Katherine Warren]
Generated description
Katherine Warren was an American character actress known for her supporting roles in mid-20th-century films and television.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Katherine Warren Target entity description: Katherine Warren was an American character actress known for her supporting roles in mid-20th-century films and television.
-
A.
Katherine Green
Katherine Green is a film editor known for her work on the romantic comedy "40 Days and 40 Nights."
-
B.
Katherine Lester
Katherine Lester is a central character in the period drama film "Lady Macbeth," known for her intense and morally complex journey portrayed by Florence Pugh.
-
C.
Katherine Swift
Katherine Swift is a notable individual recognized for her association with the Swift surname, likely distinguished in her professional or creative field.
-
D.
Catherine Faylen
Catherine Faylen is an American actress best known for her film and television roles in the 1940s and 1950s.
-
E.
Katherine Willis
Katherine Willis is an American actress known for her work in film and television, including a role in the action drama "Mercury Plains."
- 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_69d806b5a3c08190b42c267fb092f98a |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99d01bf8481908cd3a99e5557b972 |
completed | April 11, 2026, 12:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a83125d481908fe02cf85651a7bb |
completed | May 3, 2026, 7:55 p.m. |
| NEDg | Description generation | batch_69f7a8f6833881908bcca35d7d01596a |
completed | May 3, 2026, 7:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7a9c3c6548190802e1163c9c35b67 |
completed | May 3, 2026, 8:02 p.m. |
Created at: April 9, 2026, 9:31 p.m.