Triple
T14323822
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Wanda Hutchins |
E355163
|
entity |
| Predicate | marriedToFormerOccupation |
P113976
|
FINISHED |
| Object | NFL player |
—
|
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: NFL player | Statement: [Wanda Hutchins, marriedToFormerOccupation, NFL player]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToFormerOccupation Context triple: [Wanda Hutchins, marriedToFormerOccupation, NFL player]
-
A.
preMarriageOccupation
Indicates the occupation or job role a person held before getting married.
-
B.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
C.
marriedToBeforeFameOf
Indicates that one person was married to another person before the latter became famous.
-
D.
roleInSpouseCareer
Indicates the nature or extent of a person’s involvement or influence in their spouse’s professional career.
-
E.
roleDuringSpouseTenure
Indicates that a person held a particular role or position specifically during the period when their spouse was in office or serving in a defined tenure.
- 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_69d8278fa2108190bc0d0e7939c1eb03 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de883d1de88190a1e2bf2f1b692197 |
completed | April 14, 2026, 6:32 p.m. |
| PD | Predicate disambiguation | batch_69de2a9515f4819081aabf251bca5878 |
completed | April 14, 2026, 11:52 a.m. |
| PDg | Predicate description generation | batch_69de2e9ded24819099200349cf80e068 |
completed | April 14, 2026, 12:10 p.m. |
Created at: April 10, 2026, 1:13 a.m.