Triple
T16474545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. Shaw |
E400153
|
entity |
| Predicate | hasChild |
P369
|
FINISHED |
| Object |
Frank Shaw
Frank Shaw is an individual known primarily as the child of Mrs. Shaw, though further widely recognized biographical details are not specified.
|
E1225513
|
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: Frank Shaw | Statement: [Mrs. Shaw, hasChild, Frank Shaw]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Frank Shaw Context triple: [Mrs. Shaw, hasChild, Frank Shaw]
-
A.
Arthur Shaw
Arthur Shaw is the wealthy, duplicitous financier and primary antagonist targeted for revenge in the comedy heist film "Tower Heist."
-
B.
John Shaw
John Shaw is a musician best known as a member of the American noise rock band Magik Markers.
-
C.
Vernon Shaw
Vernon Shaw was a Dominican politician who served as the fifth President of the Commonwealth of Dominica from 1998 to 2003.
-
D.
Bob Shaw
Bob Shaw was a Northern Irish science fiction author and fan writer known for his witty essays, inventive concepts, and contributions to both professional and fan science fiction communities.
-
E.
Stan Shaw
Stan Shaw is an American character actor known for his roles in films such as "Harlem Nights," "Rocky," and "The Boys in Company C."
- 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: Frank Shaw Triple: [Mrs. Shaw, hasChild, Frank Shaw]
Generated description
Frank Shaw is an individual known primarily as the child of Mrs. Shaw, though further widely recognized biographical details are not specified.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Frank Shaw Target entity description: Frank Shaw is an individual known primarily as the child of Mrs. Shaw, though further widely recognized biographical details are not specified.
-
A.
Arthur Shaw
Arthur Shaw is the wealthy, duplicitous financier and primary antagonist targeted for revenge in the comedy heist film "Tower Heist."
-
B.
John Shaw
John Shaw is a musician best known as a member of the American noise rock band Magik Markers.
-
C.
Vernon Shaw
Vernon Shaw was a Dominican politician who served as the fifth President of the Commonwealth of Dominica from 1998 to 2003.
-
D.
Bob Shaw
Bob Shaw was a Northern Irish science fiction author and fan writer known for his witty essays, inventive concepts, and contributions to both professional and fan science fiction communities.
-
E.
Stan Shaw
Stan Shaw is an American character actor known for his roles in films such as "Harlem Nights," "Rocky," and "The Boys in Company C."
- 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_69d883813098819084f5409539723b59 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e32dd32e048190a9eadd32d6b9374c |
completed | April 18, 2026, 7:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0084aa47408190abe2ffaab84cdd85 |
completed | May 10, 2026, 1:14 p.m. |
| NEDg | Description generation | batch_6a0085c047f081908d7aa4b8ae5194b9 |
completed | May 10, 2026, 1:18 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a00863e69548190bb8508428c139e05 |
completed | May 10, 2026, 1:21 p.m. |
Created at: April 10, 2026, 5:13 a.m.