Triple
T13791631
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mary Shackelford |
E331411
|
entity |
| Predicate | marriedToSteveHarveyDuring |
P111478
|
FINISHED |
| Object | 1990s |
—
|
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: 1990s | Statement: [Mary Shackelford, marriedToSteveHarveyDuring, 1990s]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToSteveHarveyDuring Context triple: [Mary Shackelford, marriedToSteveHarveyDuring, 1990s]
-
A.
isFianceeOf
Indicates that one person is the engaged-to-be-married partner of another person.
-
B.
sometimesSpouseOf
Indicates that two entities are occasionally, but not consistently or permanently, in a spousal relationship with each other.
-
C.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
D.
hasSpouseInStory
Indicates that one entity is depicted as the spouse of another within the context of a particular story or narrative.
-
E.
metSpouseAt
Indicates that one person first encountered or became acquainted with their spouse at a particular place, event, or time.
- 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_69d81c58feb08190a77bca8bf7d6d20f |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de0258a1408190a837d17c6d6a2bd4 |
completed | April 14, 2026, 9:01 a.m. |
| PD | Predicate disambiguation | batch_69dbc85fb600819098a2aab48169be96 |
completed | April 12, 2026, 4:29 p.m. |
| PDg | Predicate description generation | batch_69dcad0eea9881908f71e1eed9a2446b |
completed | April 13, 2026, 8:45 a.m. |
Created at: April 9, 2026, 10:11 p.m.