Triple
T1377731
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Louise Bell |
E29262
|
entity |
| Predicate | marriedAfter |
P27121
|
FINISHED |
| Object | death of Arline Greenbaum |
—
|
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: death of Arline Greenbaum | Statement: [Mary Louise Bell, marriedAfter, death of Arline Greenbaum]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedAfter Context triple: [Mary Louise Bell, marriedAfter, death of Arline Greenbaum]
-
A.
marriedInto
Indicates that one entity became connected to another’s family or group through marriage, rather than by birth or prior membership.
-
B.
marries
Indicates that one entity enters into a legally or socially recognized marital union with another entity.
-
C.
hasMarriedSurname
Indicates that a person’s current surname is the one they adopted through marriage.
-
D.
houseByMarriage
Indicates a familial or household relationship established through marriage rather than by blood or direct residence.
-
E.
exSpouse
Indicates that two people were formerly married to each other but are no longer spouses.
- F. None of above. chosen
Provenance (4 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_69a498d883a48190bfdca525296ef7ee |
completed | March 1, 2026, 7:51 p.m. |
| NER | Named-entity recognition | batch_69a4c3173548819082aec7c1af9c577c |
completed | March 1, 2026, 10:52 p.m. |
| PD | Predicate disambiguation | batch_69a4befcabdc8190a9f05d002603f81c |
completed | March 1, 2026, 10:34 p.m. |
| PDg | Predicate description generation | batch_69a4c0335f7081908d50046ced4cdee0 |
completed | March 1, 2026, 10:39 p.m. |
Created at: March 1, 2026, 7:59 p.m.