Triple
T28566975
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Bernadette Rostenkowski-Wolowitz |
E722704
|
entity |
| Predicate | marriedInSeason |
P177317
|
FINISHED |
| Object | 5 |
—
|
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: 5 | Statement: [Bernadette Rostenkowski-Wolowitz, marriedInSeason, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedInSeason Context triple: [Bernadette Rostenkowski-Wolowitz, marriedInSeason, 5]
-
A.
marriedIn
Indicates that two entities entered into a marital relationship at a specific place or within a particular jurisdiction.
-
B.
marriageSeason
Indicates the time of year or specific season during which a marriage or wedding takes place.
-
C.
laterMarriedIn
Indicates that two entities became married at a later time relative to a previously referenced event or marital status.
-
D.
marriedInYear
Indicates that two entities are married to each other in a specific calendar year.
-
E.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
- 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_69f01a5f69d08190ad5c0d2167078dec |
completed | April 28, 2026, 2:24 a.m. |
| NER | Named-entity recognition | batch_69f6fb93224881908fc66fe76115fcdb |
completed | May 3, 2026, 7:38 a.m. |
| PD | Predicate disambiguation | batch_69f6f969b4cc8190afb473a2d8b110bc |
completed | May 3, 2026, 7:29 a.m. |
| PDg | Predicate description generation | batch_69f6fb17d5ec81909091e37e1ddbe577 |
completed | May 3, 2026, 7:36 a.m. |
Created at: April 28, 2026, 4:07 a.m.