Triple
T7913392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lauretta Geigerman |
E183754
|
entity |
| Predicate | spouseOfNotableRole |
P33561
|
FINISHED |
| Object | leader in mid-20th-century American organized crime |
—
|
LITERAL FINISHED |
How this triple was built (2 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: leader in mid-20th-century American organized crime | Statement: [Lauretta Geigerman, spouseOfNotableRole, leader in mid-20th-century American organized crime]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseOfNotableRole Context triple: [Lauretta Geigerman, spouseOfNotableRole, leader in mid-20th-century American organized crime]
-
A.
spouseNotableFor
Indicates that a person's spouse is recognized or distinguished for a particular achievement, role, or characteristic.
-
B.
spouseAssociatedWith
chosen
Indicates a marital or spousal relationship or close association between two entities.
-
C.
spouseNotableWorkField
Indicates that the notable work or professional field associated with a person’s spouse is being specified.
-
D.
spouseOfCountry
Indicates that an entity is the spouse or marital partner of a person who is associated with, represents, or is from a specified country.
-
E.
spouseAlsoKnownAs
Indicates that a person’s spouse is referred to by an alternative name or alias.
- F. None of above.
Provenance (3 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_69ca828dec0c81908b8f55a4dbbb53ff |
completed | March 30, 2026, 2:02 p.m. |
| NER | Named-entity recognition | batch_69cb3a748f4c8190bcd868de2fcf0b3a |
completed | March 31, 2026, 3:07 a.m. |
| PD | Predicate disambiguation | batch_69cae92f9498819085277879e59aa072 |
completed | March 30, 2026, 9:20 p.m. |
Created at: March 30, 2026, 5:04 p.m.