Triple
T12239824
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Eva Marie Saint |
E291699
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Jeffrey Hayden
Jeffrey Hayden was an American television and film director known for his extensive work in mid-20th-century TV dramas and variety shows.
|
E1031870
|
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: Jeffrey Hayden | Statement: [Eva Marie Saint, spouse, Jeffrey Hayden]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Hayden Context triple: [Eva Marie Saint, spouse, Jeffrey Hayden]
-
A.
Jeffrey Heath
Jeffrey Heath is a linguist renowned for his extensive fieldwork and documentation of Dogon and other African languages.
-
B.
Chris Ridenhour
Chris Ridenhour is a film composer known for scoring numerous low-budget genre movies, including works produced by The Asylum.
-
C.
Jeffrey Lynn
Jeffrey Lynn was an American film and stage actor best known for his roles in 1930s and 1940s Hollywood dramas and romances.
-
D.
Greg Hayden
Greg Hayden is a film editor best known for his work on major comedy features, including the Austin Powers series.
-
E.
Jay Hayden
Jay Hayden is an American actor best known for his role as firefighter Travis Montgomery on the television drama series "Station 19."
- 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: Jeffrey Hayden Triple: [Eva Marie Saint, spouse, Jeffrey Hayden]
Generated description
Jeffrey Hayden was an American television and film director known for his extensive work in mid-20th-century TV dramas and variety shows.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Jeffrey Hayden Target entity description: Jeffrey Hayden was an American television and film director known for his extensive work in mid-20th-century TV dramas and variety shows.
-
A.
Jeffrey Heath
Jeffrey Heath is a linguist renowned for his extensive fieldwork and documentation of Dogon and other African languages.
-
B.
Chris Ridenhour
Chris Ridenhour is a film composer known for scoring numerous low-budget genre movies, including works produced by The Asylum.
-
C.
Jeffrey Lynn
Jeffrey Lynn was an American film and stage actor best known for his roles in 1930s and 1940s Hollywood dramas and romances.
-
D.
Greg Hayden
Greg Hayden is a film editor best known for his work on major comedy features, including the Austin Powers series.
-
E.
Jay Hayden
Jay Hayden is an American actor best known for his role as firefighter Travis Montgomery on the television drama series "Station 19."
- 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_69d6ab67950c8190be08450a06228c4b |
completed | April 8, 2026, 7:24 p.m. |
| NER | Named-entity recognition | batch_69d91cb45340819093365f8efdf85f75 |
completed | April 10, 2026, 3:52 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f716afa8008190b4c518dd6004d87a |
completed | May 3, 2026, 9:34 a.m. |
| NEDg | Description generation | batch_69f7179da5488190a10acadbf60ea470 |
completed | May 3, 2026, 9:38 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f71847e7308190ac6f59a7dcafa452 |
completed | May 3, 2026, 9:41 a.m. |
Created at: April 8, 2026, 9:51 p.m.