Triple
T15909714
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mrs. George |
E385813
|
entity |
| Predicate | spouse |
P13
|
FINISHED |
| Object |
Mr. George
Mr. George is a married individual known primarily as the husband of Mrs. George.
|
E1183749
|
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: Mr. George | Statement: [Mrs. George, spouse, Mr. George]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mr. George Context triple: [Mrs. George, spouse, Mr. George]
-
A.
Grandpa George
Grandpa George is one of Charlie Bucket’s elderly, bedridden grandparents in Roald Dahl’s novel "Charlie and the Chocolate Factory."
-
B.
Mr. Brown
Mr. Brown is the kind-hearted but often flustered father figure from the "Paddington" film series.
-
C.
Mr. Brown
Mr. Brown is one of the color-coded hijackers in the crime thriller "The Taking of Pelham One Two Three," known for his role in the subway train hostage plot.
-
D.
Mr. Brown
Mr. Brown is a comically eccentric, churchgoing older man known for his loud outfits, over-the-top reactions, and frequent appearances in Tyler Perry’s Madea franchise.
-
E.
Uncle George
Uncle George is a central family figure in Eudora Welty’s novel "Delta Wedding," around whom much of the Fairchild clan’s domestic life and emotional dynamics revolve.
- 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: Mr. George Triple: [Mrs. George, spouse, Mr. George]
Generated description
Mr. George is a married individual known primarily as the husband of Mrs. George.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Mr. George Target entity description: Mr. George is a married individual known primarily as the husband of Mrs. George.
-
A.
Grandpa George
Grandpa George is one of Charlie Bucket’s elderly, bedridden grandparents in Roald Dahl’s novel "Charlie and the Chocolate Factory."
-
B.
Mr. Brown
Mr. Brown is the kind-hearted but often flustered father figure from the "Paddington" film series.
-
C.
Mr. Brown
Mr. Brown is one of the color-coded hijackers in the crime thriller "The Taking of Pelham One Two Three," known for his role in the subway train hostage plot.
-
D.
Mr. Brown
Mr. Brown is a comically eccentric, churchgoing older man known for his loud outfits, over-the-top reactions, and frequent appearances in Tyler Perry’s Madea franchise.
-
E.
Uncle George
Uncle George is a central family figure in Eudora Welty’s novel "Delta Wedding," around whom much of the Fairchild clan’s domestic life and emotional dynamics revolve.
- 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_69d86da686e4819097cbf3b1fc2d881d |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e1565ea7a8819097efffda366b5245 |
completed | April 16, 2026, 9:36 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffb05750ac81908860143f4ca26cc7 |
completed | May 9, 2026, 10:08 p.m. |
| NEDg | Description generation | batch_69ffb110a5b88190904f763057e8eb1e |
completed | May 9, 2026, 10:11 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ffb1a5e9b88190b790c81b9500c2ac |
completed | May 9, 2026, 10:13 p.m. |
Created at: April 10, 2026, 4:52 a.m.