Triple
T22972962
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Garage Days |
E571236
|
entity |
| Predicate | starredActor |
P5563
|
FINISHED |
| Object |
Maya Stange
Maya Stange is an Australian actress known for her roles in film and television, including prominent performances in early-2000s indie and drama projects.
|
E1565714
|
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: Maya Stange | Statement: [Garage Days, starredActor, Maya Stange]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Maya Stange Context triple: [Garage Days, starredActor, Maya Stange]
-
A.
Maya Hansen
Maya Hansen is a brilliant botanist and geneticist in the Marvel Cinematic Universe whose Extremis research plays a pivotal role in the events of Iron Man 3.
-
B.
Maya Imhoof
Maya Imhoof is a film producer best known for her work on the acclaimed Swiss drama "The Boat Is Full."
-
C.
Maya Pfaff
Maya Pfaff is a member of the extended Bose–Pfaff family, descended from Indian nationalist leader Subhas Chandra Bose through his daughter Anita Bose Pfaff.
-
D.
Kirsten Vangsness
Kirsten Vangsness is an American actress best known for her role as technical analyst Penelope Garcia on the television series "Criminal Minds."
-
E.
Tammara Draut
Tammara Draut is an American higher education leader who serves as president of the University of Indianapolis.
- 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: Maya Stange Triple: [Garage Days, starredActor, Maya Stange]
Generated description
Maya Stange is an Australian actress known for her roles in film and television, including prominent performances in early-2000s indie and drama projects.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Maya Stange Target entity description: Maya Stange is an Australian actress known for her roles in film and television, including prominent performances in early-2000s indie and drama projects.
-
A.
Maya Hansen
Maya Hansen is a brilliant botanist and geneticist in the Marvel Cinematic Universe whose Extremis research plays a pivotal role in the events of Iron Man 3.
-
B.
Maya Imhoof
Maya Imhoof is a film producer best known for her work on the acclaimed Swiss drama "The Boat Is Full."
-
C.
Maya Pfaff
Maya Pfaff is a member of the extended Bose–Pfaff family, descended from Indian nationalist leader Subhas Chandra Bose through his daughter Anita Bose Pfaff.
-
D.
Kirsten Vangsness
Kirsten Vangsness is an American actress best known for her role as technical analyst Penelope Garcia on the television series "Criminal Minds."
-
E.
Tammara Draut
Tammara Draut is an American higher education leader who serves as president of the University of Indianapolis.
- 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_69e245b2c6548190a0e4c7f2f7df2d48 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f182343a448190a5259cdf721c9d04 |
completed | April 29, 2026, 3:59 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bd36dc8d08190aa691002025a19c4 |
completed | May 19, 2026, 3:05 a.m. |
| NEDg | Description generation | batch_6a0bdc7182cc8190909ca3de14162767 |
completed | May 19, 2026, 3:43 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bdd06e8e88190bbd43d0c877a74ff |
completed | May 19, 2026, 3:46 a.m. |
Created at: April 17, 2026, 3:48 p.m.