Triple
T22172308
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | The Window |
E547952
|
entity |
| Predicate | mainCharacter |
P1183
|
FINISHED |
| Object |
Tommy Woodry
Tommy Woodry is the young boy protagonist of the 1949 film noir "The Window," whose eyewitness account of a crime drives the suspenseful plot.
|
E1524159
|
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: Tommy Woodry | Statement: [The Window, mainCharacter, Tommy Woodry]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tommy Woodry Context triple: [The Window, mainCharacter, Tommy Woodry]
-
A.
Sherman Kell
Sherman Kell is a film editor known for his work on the classic silent comedy "The Cameraman."
-
B.
Jim McClain
Jim McClain is a writer known for creating the story for the work titled "Robots."
-
C.
Todd Duffey
Todd Duffey is an American actor best known for playing the overenthusiastic waiter Brian in the cult comedy film "Office Space."
-
D.
Tommy Tilden
Tommy Tilden is a small-town coroner and widowed father who becomes entangled in a terrifying supernatural mystery while performing an autopsy in the horror film "The Autopsy of Jane Doe."
-
E.
Hank McCamish
Hank McCamish was a prominent Atlanta insurance executive and philanthropist whose support for Georgia Tech athletics led to the university’s basketball arena being named in his honor.
- 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: Tommy Woodry Triple: [The Window, mainCharacter, Tommy Woodry]
Generated description
Tommy Woodry is the young boy protagonist of the 1949 film noir "The Window," whose eyewitness account of a crime drives the suspenseful plot.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Tommy Woodry Target entity description: Tommy Woodry is the young boy protagonist of the 1949 film noir "The Window," whose eyewitness account of a crime drives the suspenseful plot.
-
A.
Sherman Kell
Sherman Kell is a film editor known for his work on the classic silent comedy "The Cameraman."
-
B.
Jim McClain
Jim McClain is a writer known for creating the story for the work titled "Robots."
-
C.
Todd Duffey
Todd Duffey is an American actor best known for playing the overenthusiastic waiter Brian in the cult comedy film "Office Space."
-
D.
Tommy Tilden
Tommy Tilden is a small-town coroner and widowed father who becomes entangled in a terrifying supernatural mystery while performing an autopsy in the horror film "The Autopsy of Jane Doe."
-
E.
Hank McCamish
Hank McCamish was a prominent Atlanta insurance executive and philanthropist whose support for Georgia Tech athletics led to the university’s basketball arena being named in his honor.
- 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_69e11e3d53f88190a2b690e3f25bb062 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f12a69c12c8190a03177b5b740456a |
completed | April 28, 2026, 9:45 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0aa606177081908b30cff5514cd8c7 |
completed | May 18, 2026, 5:39 a.m. |
| NEDg | Description generation | batch_6a0aa754e2f08190ad4b2186ab0994b3 |
completed | May 18, 2026, 5:44 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0aa79ada5881908b6a6a86290f119e |
completed | May 18, 2026, 5:46 a.m. |
Created at: April 16, 2026, 8:34 p.m.