Triple
T13773941
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Iron Will |
E330951
|
entity |
| Predicate | hasCastMember |
P2308
|
FINISHED |
| Object |
Penelope Windust
Penelope Windust was an American actress known for her work in film and television, including roles in projects such as the miniseries "V."
|
E1059969
|
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: Penelope Windust | Statement: [Iron Will, hasCastMember, Penelope Windust]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Penelope Windust Context triple: [Iron Will, hasCastMember, Penelope Windust]
-
A.
Penelope Highton
Penelope Highton is known as the spouse of American poet Robert Creeley.
-
B.
Penelope Milford
Penelope Milford is an American actress best known for her Academy Award–nominated supporting role in the 1978 film "Coming Home."
-
C.
Penelope Keith
Penelope Keith is an English actress best known for her roles in classic British television sitcoms such as "The Good Life" and "To the Manor Born."
-
D.
Penelope Allen
Penelope Allen is an American actress best known for her supporting role in the 1975 crime drama film "Dog Day Afternoon."
-
E.
Penelope Taynt
Penelope Taynt is a fictional, obsessively devoted fan character and comedic stalker of Amanda Bynes on the Nickelodeon sketch comedy series "The Amanda Show."
- 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: Penelope Windust Triple: [Iron Will, hasCastMember, Penelope Windust]
Generated description
Penelope Windust was an American actress known for her work in film and television, including roles in projects such as the miniseries "V."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Penelope Windust Target entity description: Penelope Windust was an American actress known for her work in film and television, including roles in projects such as the miniseries "V."
-
A.
Penelope Highton
Penelope Highton is known as the spouse of American poet Robert Creeley.
-
B.
Penelope Milford
Penelope Milford is an American actress best known for her Academy Award–nominated supporting role in the 1978 film "Coming Home."
-
C.
Penelope Keith
Penelope Keith is an English actress best known for her roles in classic British television sitcoms such as "The Good Life" and "To the Manor Born."
-
D.
Penelope Allen
Penelope Allen is an American actress best known for her supporting role in the 1975 crime drama film "Dog Day Afternoon."
-
E.
Penelope Taynt
Penelope Taynt is a fictional, obsessively devoted fan character and comedic stalker of Amanda Bynes on the Nickelodeon sketch comedy series "The Amanda Show."
- 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_69d81c583b0081909e408a17db517a21 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de023774b48190b19e43e87b94ba77 |
completed | April 14, 2026, 9 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7a86afd788190ab637044dd489a24 |
completed | May 3, 2026, 7:56 p.m. |
| NEDg | Description generation | batch_69f7a9f5549c81908a1a0b080acc3396 |
completed | May 3, 2026, 8:03 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7aafceea881908737a3d7613db2d5 |
completed | May 3, 2026, 8:07 p.m. |
Created at: April 9, 2026, 10:10 p.m.