Triple
T7257157
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | George Englund |
E157749
|
entity |
| Predicate | startTime (marriage to Cloris Leachman) |
P75595
|
FINISHED |
| Object | 1953 |
—
|
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: 1953 | Statement: [George Englund, startTime (marriage to Cloris Leachman), 1953]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startTime (marriage to Cloris Leachman) Context triple: [George Englund, startTime (marriage to Cloris Leachman), 1953]
-
A.
start time of marriage with Humphrey Bogart
Indicates the date and time when a person’s marriage to Humphrey Bogart began.
-
B.
end time of marriage with Humphrey Bogart
Indicates the date and time at which a person’s marriage to Humphrey Bogart ended.
-
C.
startTimeOfMarriageWithCaryGrant
Indicates the date and time when an individual’s marriage to Cary Grant began.
-
D.
startTime (marriage to Nero)
Indicates the point in time when the marriage to Nero began.
-
E.
marriageStartWithMarilynMonroe
Indicates the point in time when an entity begins a marriage with Marilyn Monroe.
- 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_69c6882d81d4819085f7ff862951ee4f |
completed | March 27, 2026, 1:37 p.m. |
| NER | Named-entity recognition | batch_69c6eaa274bc8190b017b71583711453 |
completed | March 27, 2026, 8:37 p.m. |
| PD | Predicate disambiguation | batch_69c6e7666ffc81908bf643d8257e6337 |
completed | March 27, 2026, 8:24 p.m. |
| PDg | Predicate description generation | batch_69c6e889854481908c765ce2107f2d3a |
completed | March 27, 2026, 8:28 p.m. |
Created at: March 27, 2026, 2:57 p.m.