Triple
T21485686
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Harriet Ann Taylor |
E530109
|
entity |
| Predicate | marriedToProfession |
P144544
|
FINISHED |
| Object | economist |
—
|
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: economist | Statement: [Harriet Ann Taylor, marriedToProfession, economist]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToProfession Context triple: [Harriet Ann Taylor, marriedToProfession, economist]
-
A.
marriedToFormerOccupation
Indicates that a person is married to someone who previously held a specified occupation.
-
B.
marriedToBusinessperson
Indicates that one entity is married to another entity who is a businessperson.
-
C.
marriedToBeforeFameOf
Indicates that one person was married to another person before the latter became famous.
-
D.
marriedBy
Indicates that one entity is the officiant or authority who performs and formalizes the marriage of another entity.
-
E.
roleInSpouseCareer
Indicates the nature or extent of a person’s involvement or influence in their spouse’s professional career.
- 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_69e0c45acc3881908e38d3f28964152b |
completed | April 16, 2026, 11:13 a.m. |
| NER | Named-entity recognition | batch_69e9ea370adc8190b79fe26e2eba1dca |
completed | April 23, 2026, 9:45 a.m. |
| PD | Predicate disambiguation | batch_69e631ec1d048190b6da97da8222e413 |
completed | April 20, 2026, 2:02 p.m. |
| PDg | Predicate description generation | batch_69e6386c5a4481909c37f7de7e9fc025 |
completed | April 20, 2026, 2:30 p.m. |
Created at: April 16, 2026, 6:21 p.m.