Triple
T1676639
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Jennifer Garner |
E36244
|
entity |
| Predicate | marriageStartWithBenAffleck |
P31638
|
FINISHED |
| Object | 2005 |
—
|
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: 2005 | Statement: [Jennifer Garner, marriageStartWithBenAffleck, 2005]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageStartWithBenAffleck Context triple: [Jennifer Garner, marriageStartWithBenAffleck, 2005]
-
A.
marriageStartWithLaurenceOlivier
Indicates the point in time when a marriage involving Laurence Olivier began.
-
B.
marriedToNotablePerson
Indicates that a person is legally married to another individual who is widely recognized or notable.
-
C.
marries
Indicates that one entity enters into a legally or socially recognized marital union with another entity.
-
D.
marriageToCharlieChaplinStart
Indicates the point in time when an entity begins being married to Charlie Chaplin.
-
E.
ageAtMarriage
Indicates the age a person was when they got married.
- 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_69a886139ed081909af0940aa9313512 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69ab272a653481908f48aa1eed5de8a4 |
completed | March 6, 2026, 7:12 p.m. |
| PD | Predicate disambiguation | batch_69aa61b2f6288190b2348ef7d7e4672d |
completed | March 6, 2026, 5:10 a.m. |
| PDg | Predicate description generation | batch_69ab271be3f4819091adcd745dec8159 |
completed | March 6, 2026, 7:12 p.m. |
Created at: March 4, 2026, 7:29 p.m.