Triple
T13262094
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lenore Kipp |
E315822
|
entity |
| Predicate | marriageDurationCharacteristic |
P57236
|
FINISHED |
| Object | long-term marriage |
—
|
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: long-term marriage | Statement: [Lenore Kipp, marriageDurationCharacteristic, long-term marriage]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageDurationCharacteristic Context triple: [Lenore Kipp, marriageDurationCharacteristic, long-term marriage]
-
A.
marriageDuration
chosen
Indicates the length of time that a marriage relationship has existed between two spouses.
-
B.
maritalPeriodWith
Indicates the time span during which two entities were married to each other.
-
C.
marriageDate
Indicates the specific date on which two entities entered into a marital relationship.
-
D.
marriageType
Indicates the specific legal or social category of a marriage relationship that exists between two spouses.
-
E.
marriageOutcome
Indicates the result or status that follows from a marriage, such as whether it continues, ends, or changes form.
- 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_69d806b1d9ac8190852c5571d5bd5f0f |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d99cfdc9388190af1fdd3cd4717bd8 |
completed | April 11, 2026, 12:59 a.m. |
| PD | Predicate disambiguation | batch_69d98f60911081909fa346a054f76c9f |
completed | April 11, 2026, 12:01 a.m. |
Created at: April 9, 2026, 9:25 p.m.