Triple
T16069370
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | King Kong Escapes |
E389817
|
entity |
| Predicate | starring |
P1507
|
FINISHED |
| Object |
Linda Miller
Linda Miller is an American actress best known for her leading role in the 1967 Japanese-American monster film "King Kong Escapes."
|
E1458846
|
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: Linda Miller | Statement: [King Kong Escapes, starring, Linda Miller]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Linda Miller Context triple: [King Kong Escapes, starring, Linda Miller]
-
A.
Linda Moran
Linda Moran is a film producer known for her work on the critically acclaimed independent drama "Transamerica."
-
B.
Linda Stokes
Linda Stokes is an American costume designer best known for her long-term marriage to actor James Caan.
-
C.
Patricia Miller
Patricia Miller is known primarily as the wife of American attorney Herbert J. Miller Jr., who was involved in several high-profile political legal cases.
-
D.
Linda Wallem
Linda Wallem is an American television writer, producer, and actress best known for co-creating the acclaimed medical dramedy series "Nurse Jackie."
-
E.
Shirley Miller
Shirley Miller is the central protagonist of the crime drama film "Widows," a recently widowed woman who becomes the leader of a heist planned to settle her late husband's debts.
- 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: Linda Miller Triple: [King Kong Escapes, starring, Linda Miller]
Generated description
Linda Miller is an American actress best known for her leading role in the 1967 Japanese-American monster film "King Kong Escapes."
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Linda Miller Target entity description: Linda Miller is an American actress best known for her leading role in the 1967 Japanese-American monster film "King Kong Escapes."
-
A.
Linda Moran
Linda Moran is a film producer known for her work on the critically acclaimed independent drama "Transamerica."
-
B.
Linda Stokes
Linda Stokes is an American costume designer best known for her long-term marriage to actor James Caan.
-
C.
Patricia Miller
Patricia Miller is known primarily as the wife of American attorney Herbert J. Miller Jr., who was involved in several high-profile political legal cases.
-
D.
Linda Wallem
Linda Wallem is an American television writer, producer, and actress best known for co-creating the acclaimed medical dramedy series "Nurse Jackie."
-
E.
Shirley Miller
Shirley Miller is the central protagonist of the crime drama film "Widows," a recently widowed woman who becomes the leader of a heist planned to settle her late husband's debts.
- 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_69d86daf32ec8190a8c0466c8f49c3c0 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e183bb98c88190ae4b5773358078be |
completed | April 17, 2026, 12:50 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a09276e98588190ad3bdb9d04a19584 |
completed | May 17, 2026, 2:26 a.m. |
| NEDg | Description generation | batch_6a0928a53e3c8190a8e636f5f1387f73 |
completed | May 17, 2026, 2:32 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0929485ebc8190ab8bc316b3e802ca |
completed | May 17, 2026, 2:34 a.m. |
Created at: April 10, 2026, 4:57 a.m.