Triple
T11599300
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Alisande Ullman |
E275084
|
entity |
| Predicate | maritalStatusWithLeslieNielsen |
P5173
|
FINISHED |
| Object | divorced |
—
|
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: divorced | Statement: [Alisande Ullman, maritalStatusWithLeslieNielsen, divorced]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: maritalStatusWithLeslieNielsen Context triple: [Alisande Ullman, maritalStatusWithLeslieNielsen, divorced]
-
A.
spouseAssociatedWith
Indicates a marital or spousal relationship or close association between two entities.
-
B.
spouseStatus
chosen
Indicates the marital relationship status between two individuals, such as whether they are currently spouses, formerly spouses, or not married to each other.
-
C.
spouseNotableFor
Indicates that a person's spouse is recognized or distinguished for a particular achievement, role, or characteristic.
-
D.
marital status
Indicates the legal or social state of a person’s marriage-related relationship, such as being single, married, divorced, or widowed.
-
E.
characterMaritalHistory
Indicates a relationship that records the sequence of a character’s past and present marital relationships, including spouses and relevant time periods.
- 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_69d6aae6b14c81908dc5a74bad7591f9 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8954c3c248190bcccd4c7ff667b3a |
completed | April 10, 2026, 6:14 a.m. |
| PD | Predicate disambiguation | batch_69d85dd20d188190863d1190d4c16048 |
completed | April 10, 2026, 2:17 a.m. |
Created at: April 8, 2026, 9:38 p.m.