Triple
T8960209
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Catherine Keener |
E213981
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Keener
Keener is a surname most prominently associated with American actress Catherine Keener, known for her acclaimed roles in independent and mainstream films.
|
E770356
|
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: Keener | Statement: [Catherine Keener, familyName, Keener]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Keener Context triple: [Catherine Keener, familyName, Keener]
-
A.
Keent
Keent is a small rural hamlet in the Dutch province of North Brabant, known for its riverine landscape and nature restoration areas along the Maas.
-
B.
Keen
Keen is a surname of English origin borne by various notable individuals across fields such as acting, sports, and academia.
-
C.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
D.
Kuper
Kuper is a variant form of the name Cooper, typically used as a surname or given name.
-
E.
Keefe
Keefe is a surname most prominently associated with Sheldon Keefe, a Canadian professional ice hockey coach and former player.
- 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: Keener Triple: [Catherine Keener, familyName, Keener]
Generated description
Keener is a surname most prominently associated with American actress Catherine Keener, known for her acclaimed roles in independent and mainstream films.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Keener Target entity description: Keener is a surname most prominently associated with American actress Catherine Keener, known for her acclaimed roles in independent and mainstream films.
-
A.
Keent
Keent is a small rural hamlet in the Dutch province of North Brabant, known for its riverine landscape and nature restoration areas along the Maas.
-
B.
Keen
Keen is a surname of English origin borne by various notable individuals across fields such as acting, sports, and academia.
-
C.
Keefer
Keefer was a distinguished racing greyhound renowned for its achievements on the track, earning induction into the Greyhound Hall of Fame.
-
D.
Kuper
Kuper is a variant form of the name Cooper, typically used as a surname or given name.
-
E.
Keefe
Keefe is a surname most prominently associated with Sheldon Keefe, a Canadian professional ice hockey coach and former player.
- 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_69ca839cd6008190a1546a701a56710c |
completed | March 30, 2026, 2:07 p.m. |
| NER | Named-entity recognition | batch_69cc6746fbf88190aba658b4b9c2e4b0 |
completed | April 1, 2026, 12:31 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69cfc94ab868819080ff3fc7532a3874 |
completed | April 3, 2026, 2:06 p.m. |
| NEDg | Description generation | batch_69cfcd33936c8190bcbb0861e330b895 |
completed | April 3, 2026, 2:22 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69cfcdd61ff8819097ca9662aa42cb4a |
completed | April 3, 2026, 2:25 p.m. |
Created at: March 30, 2026, 7 p.m.