Triple
T9459578
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Max von Sydow |
E228106
|
entity |
| Predicate | marriageStartWithCatherineBrelet |
P89059
|
FINISHED |
| Object | 1997 |
—
|
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: 1997 | Statement: [Max von Sydow, marriageStartWithCatherineBrelet, 1997]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: marriageStartWithCatherineBrelet Context triple: [Max von Sydow, marriageStartWithCatherineBrelet, 1997]
-
A.
marriageStartWithElizabethTaylor
Indicates the point in time when a marriage involving Elizabeth Taylor began.
-
B.
marriageToHenryVIIIStatus
Indicates the status or condition of an entity’s marriage relationship to Henry VIII (e.g., current, former, annulled, pending, etc.).
-
C.
marriageToHenryIIDate
Indicates the date on which an entity entered into marriage with Henry II.
-
D.
marriedToFutureMonarch
Indicates that one person is married to another person who will become a monarch in the future.
-
E.
marriageStartWithPompey
Indicates the point in time or event at which a marriage involving Pompey begins.
- 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_69ca843b123881909b0e60028475d12d |
completed | March 30, 2026, 2:10 p.m. |
| NER | Named-entity recognition | batch_69cd7fc916348190aeb3874a89071677 |
completed | April 1, 2026, 8:27 p.m. |
| PD | Predicate disambiguation | batch_69cca55caaa8819089c5138e014892d3 |
completed | April 1, 2026, 4:55 a.m. |
| PDg | Predicate description generation | batch_69ccbf9b080c819098934a18cf2bac5d |
completed | April 1, 2026, 6:47 a.m. |
Created at: March 30, 2026, 7:52 p.m.