Triple
T38604460
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Second Johnson ministry |
E934303
|
entity |
| Predicate | numberOfWomenInCabinetAtFormation |
P196711
|
FINISHED |
| Object | about 7 |
—
|
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: about 7 | Statement: [Second Johnson ministry, numberOfWomenInCabinetAtFormation, about 7]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: numberOfWomenInCabinetAtFormation Context triple: [Second Johnson ministry, numberOfWomenInCabinetAtFormation, about 7]
-
A.
hasNumberOfWomenMinisters
chosen
Indicates the minimum number of women serving as ministers in a given government or governing body.
-
B.
numberOfMenInFirstCabinet
Indicates the count of male members in the first-formed cabinet of a given government or administration.
-
C.
numberOfCabinetMembers
Indicates the total count of cabinet members associated with a given government, administration, or leader.
-
D.
hasNumberOfMinisters
Indicates the specific count of ministers associated with an entity, such as a government, cabinet, or organization.
-
E.
numberOfWomenCountyMembers
Indicates the count of women who are members within a given county.
- 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_69f76ecc17688190b389b693a5927501 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037c9141dc819098d7fcc36e69882c |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a037a2026248190b894436a578d79ac |
completed | May 12, 2026, 7:06 p.m. |
Created at: May 3, 2026, 4:32 p.m.