Triple
T13835359
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anne Buydens |
E332510
|
entity |
| Predicate | spouseMarriageDurationYears |
P57236
|
FINISHED |
| Object | 66 |
—
|
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: 66 | Statement: [Anne Buydens, spouseMarriageDurationYears, 66]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: spouseMarriageDurationYears Context triple: [Anne Buydens, spouseMarriageDurationYears, 66]
-
A.
marriageDuration
chosen
Indicates the length of time that a marriage relationship has existed between two spouses.
-
B.
maritalPeriodWith
Indicates the time span during which two entities were married to each other.
-
C.
marriedInYear
Indicates that two entities are married to each other in a specific calendar year.
-
D.
spouseOfSince
Indicates that two individuals are spouses and specifies the date or time from which their marital relationship has been in effect.
-
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.
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_69d81c5ae7c88190b0dd41bdafeb5999 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de029b352081909605baaedc336213 |
completed | April 14, 2026, 9:02 a.m. |
| PD | Predicate disambiguation | batch_69dbc86668e08190ba9135d1c3f38d35 |
completed | April 12, 2026, 4:29 p.m. |
Created at: April 9, 2026, 10:13 p.m.