Triple
T4457356
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Charles Grodin |
E97760
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Elissa Durwood Grodin
Elissa Durwood Grodin is an American author known for writing mystery novels and children's books.
|
E507088
|
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: Elissa Durwood Grodin | Statement: [Charles Grodin, spouse, Elissa Durwood Grodin]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Elissa Durwood Grodin Context triple: [Charles Grodin, spouse, Elissa Durwood Grodin]
-
A.
Roberta Seidman
Roberta Seidman was the wife of American actor John Garfield, a prominent film star of the 1930s and 1940s.
-
B.
Liz Gorinsky
Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
-
C.
Carolee Joyce Winstein
Carolee Joyce Winstein is an American neuroscientist and rehabilitation researcher known for her work on motor control and recovery after neurological injury.
-
D.
Sari Gilman
Sari Gilman is a film editor best known for her work on the Academy Award–winning documentary "Taxi to the Dark Side."
-
E.
Judith Gellman
Judith Gellman is a costume designer best known for her work on the 1995 film adaptation of "A Little Princess."
- 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: Elissa Durwood Grodin Triple: [Charles Grodin, spouse, Elissa Durwood Grodin]
Generated description
Elissa Durwood Grodin is an American author known for writing mystery novels and children's books.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Elissa Durwood Grodin Target entity description: Elissa Durwood Grodin is an American author known for writing mystery novels and children's books.
-
A.
Roberta Seidman
Roberta Seidman was the wife of American actor John Garfield, a prominent film star of the 1930s and 1940s.
-
B.
Liz Gorinsky
Liz Gorinsky is an acclaimed science fiction and fantasy editor known for her influential work at Tor Books and for winning major genre awards.
-
C.
Carolee Joyce Winstein
Carolee Joyce Winstein is an American neuroscientist and rehabilitation researcher known for her work on motor control and recovery after neurological injury.
-
D.
Sari Gilman
Sari Gilman is a film editor best known for her work on the Academy Award–winning documentary "Taxi to the Dark Side."
-
E.
Judith Gellman
Judith Gellman is a costume designer best known for her work on the 1995 film adaptation of "A Little Princess."
- 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_69b3454777808190b78aa9047ba1f018 |
completed | March 12, 2026, 10:59 p.m. |
| NER | Named-entity recognition | batch_69b356434e9481908f883c09e0908f6b |
completed | March 13, 2026, 12:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69befe41822c8190b406c192170af3d1 |
completed | March 21, 2026, 8:23 p.m. |
| NEDg | Description generation | batch_69beff4322308190b252820e7213f05e |
completed | March 21, 2026, 8:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69beffe02d208190b857d6aaa4d85dae |
completed | March 21, 2026, 8:30 p.m. |
Created at: March 12, 2026, 11:33 p.m.