Triple
T22977247
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Laird Becker |
E571358
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Allison Becker
Allison Becker is the spouse of Laird Becker, about whom little public biographical information is widely available.
|
E1564793
|
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: Allison Becker | Statement: [Laird Becker, spouse, Allison Becker]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Allison Becker Context triple: [Laird Becker, spouse, Allison Becker]
-
A.
Allison Becker
Allison Becker is a fictional character in the 2023 romantic comedy film "No Hard Feelings."
-
B.
Natalie Becker
Natalie Becker is a South African actress known for her roles in international films and television, including action and fantasy productions.
-
C.
Megan Beyer
Megan Beyer is an American journalist and civic leader known for her work in cultural diplomacy, gender equality, and public policy initiatives.
-
D.
Allison Feaster
Allison Feaster is a former American professional basketball player best known for her standout WNBA career and later work as an NBA front office executive.
-
E.
Allie Stolz
Allie Stolz was an American lightweight boxer active in the 1930s and 1940s, known for competing against several top contenders of his era.
- 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: Allison Becker Triple: [Laird Becker, spouse, Allison Becker]
Generated description
Allison Becker is the spouse of Laird Becker, about whom little public biographical information is widely available.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Allison Becker Target entity description: Allison Becker is the spouse of Laird Becker, about whom little public biographical information is widely available.
-
A.
Allison Becker
Allison Becker is a fictional character in the 2023 romantic comedy film "No Hard Feelings."
-
B.
Natalie Becker
Natalie Becker is a South African actress known for her roles in international films and television, including action and fantasy productions.
-
C.
Megan Beyer
Megan Beyer is an American journalist and civic leader known for her work in cultural diplomacy, gender equality, and public policy initiatives.
-
D.
Allison Feaster
Allison Feaster is a former American professional basketball player best known for her standout WNBA career and later work as an NBA front office executive.
-
E.
Allie Stolz
Allie Stolz was an American lightweight boxer active in the 1930s and 1940s, known for competing against several top contenders of his era.
- 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_69e245b3c50481908bb3741ec9f40862 |
completed | April 17, 2026, 2:37 p.m. |
| NER | Named-entity recognition | batch_69f18292f3788190ab4e9d559e0070c8 |
completed | April 29, 2026, 4:01 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0bd36f73a48190ba07a754949e2e40 |
completed | May 19, 2026, 3:05 a.m. |
| NEDg | Description generation | batch_6a0bd41c89708190a3df2a798ca25c99 |
completed | May 19, 2026, 3:08 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a0bd5263734819082ce8b1c3082243f |
completed | May 19, 2026, 3:12 a.m. |
Created at: April 17, 2026, 3:48 p.m.