Triple
T20469540
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stacy Haiduk |
E502150
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Bradley Hall
Bradley Hall is an American actor known for his work in film and television and for being married to actress Stacy Haiduk.
|
E1433306
|
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: Bradley Hall | Statement: [Stacy Haiduk, spouse, Bradley Hall]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Bradley Hall Context triple: [Stacy Haiduk, spouse, Bradley Hall]
-
A.
Parmenter Hall
Parmenter Hall is a historic academic building on the campus of Baker University in Baldwin City, Kansas.
-
B.
Lewis Hall
Lewis Hall is a building located adjacent to Latimer Hall, likely part of an academic or institutional campus.
-
C.
Hollis Hall
Hollis Hall is one of Harvard University's historic brick dormitories, located in Harvard Yard and traditionally housing first-year students.
-
D.
Warren Hall
Warren Hall is a building located on the campus of Rensselaer Polytechnic Institute in Troy, New York.
-
E.
Warren Hall
Warren Hall is the main academic building of the University of San Diego School of Law, housing its classrooms, faculty offices, and key administrative and student services.
- 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: Bradley Hall Triple: [Stacy Haiduk, spouse, Bradley Hall]
Generated description
Bradley Hall is an American actor known for his work in film and television and for being married to actress Stacy Haiduk.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Bradley Hall Target entity description: Bradley Hall is an American actor known for his work in film and television and for being married to actress Stacy Haiduk.
-
A.
Parmenter Hall
Parmenter Hall is a historic academic building on the campus of Baker University in Baldwin City, Kansas.
-
B.
Lewis Hall
Lewis Hall is a building located adjacent to Latimer Hall, likely part of an academic or institutional campus.
-
C.
Hollis Hall
Hollis Hall is one of Harvard University's historic brick dormitories, located in Harvard Yard and traditionally housing first-year students.
-
D.
Warren Hall
Warren Hall is the main academic building of the University of San Diego School of Law, housing its classrooms, faculty offices, and key administrative and student services.
-
E.
Warren Hall
Warren Hall is a building located on the campus of Rensselaer Polytechnic Institute in Troy, New York.
- 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_69e0b4ae5f1081908768b0c9a3a0bf38 |
completed | April 16, 2026, 10:06 a.m. |
| NER | Named-entity recognition | batch_69e6995f753081909bbe03f7c251d9c1 |
completed | April 20, 2026, 9:23 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a088b1c8764819082fab819e2114156 |
completed | May 16, 2026, 3:19 p.m. |
| NEDg | Description generation | batch_6a088c75b09081908a35bc7a9b6a45ce |
completed | May 16, 2026, 3:25 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a088d8c3ab08190a399dba52e3c99f5 |
completed | May 16, 2026, 3:30 p.m. |
Created at: April 16, 2026, 11:33 a.m.