Triple
T12809769
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danielle Émilienne Isabelle Gouze |
E306239
|
entity |
| Predicate | startTimeAsFirstLadyOfFrance |
P106498
|
FINISHED |
| Object | 1981 |
—
|
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: 1981 | Statement: [Danielle Émilienne Isabelle Gouze, startTimeAsFirstLadyOfFrance, 1981]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: startTimeAsFirstLadyOfFrance Context triple: [Danielle Émilienne Isabelle Gouze, startTimeAsFirstLadyOfFrance, 1981]
-
A.
reignAsQueenOfFranceStart
Indicates the time or event at which an individual begins her tenure as Queen of France.
-
B.
reignAsQueenConsortOfFranceStart
Indicates the time at which a person began serving as queen consort of France.
-
C.
reignAsQueenOfFranceEnd
Indicates the time or event at which an individual’s tenure as Queen of France comes to an end.
-
D.
successorAsEmpressOfTheFrench
Indicates that one person became the next Empress of the French following another person in that imperial role.
-
E.
predecessorAsEmpressOfTheFrench
Indicates that one person previously held the title Empress of the French before another person, in a direct succession.
- 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_69d7bdf46c448190b1faa55aaacb6317 |
completed | April 9, 2026, 2:55 p.m. |
| NER | Named-entity recognition | batch_69d96e817598819080fdd61e9d61236e |
completed | April 10, 2026, 9:41 p.m. |
| PD | Predicate disambiguation | batch_69d9640ed7448190b276e7fab649f7d2 |
completed | April 10, 2026, 8:56 p.m. |
| PDg | Predicate description generation | batch_69d96d88be0481908c311f1e71b61e70 |
completed | April 10, 2026, 9:37 p.m. |
Created at: April 9, 2026, 5:31 p.m.