Triple
T1236389
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Elizabeth Sydenham |
E26556
|
entity |
| Predicate | marriedToOccupation |
P4765
|
FINISHED |
| Object | naval commander |
—
|
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: naval commander | Statement: [Elizabeth Sydenham, marriedToOccupation, naval commander]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriedToOccupation Context triple: [Elizabeth Sydenham, marriedToOccupation, naval commander]
-
A.
spouseOccupation
chosen
Indicates that one person’s spouse has a particular job, profession, or occupation.
-
B.
spouseNotableWorkField
Indicates that the notable work or professional field associated with a person’s spouse is being specified.
-
C.
marriedInto
Indicates that one entity became connected to another’s family or group through marriage, rather than by birth or prior membership.
-
D.
spouseNotableFor
Indicates that a person's spouse is recognized or distinguished for a particular achievement, role, or characteristic.
-
E.
hasSpouseTitle
Indicates that a person’s spouse holds a particular title or honorific designation.
- F. None of above.
Provenance (3 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_69a4948571c88190a9191e451e6035fd |
completed | March 1, 2026, 7:33 p.m. |
| NER | Named-entity recognition | batch_69a4bf17e0bc8190a066561e6b629fc0 |
completed | March 1, 2026, 10:35 p.m. |
| PD | Predicate disambiguation | batch_69a4bb67d52c8190815d6356b79d6ed5 |
completed | March 1, 2026, 10:19 p.m. |
Created at: March 1, 2026, 7:47 p.m.