Triple
T7756525
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mr. Prime Minister |
E175910
|
entity |
| Predicate | relatedFemaleForm |
P52140
|
FINISHED |
| Object | Madam Prime Minister |
—
|
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: Madam Prime Minister | Statement: [Mr. Prime Minister, relatedFemaleForm, Madam Prime Minister]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: relatedFemaleForm Context triple: [Mr. Prime Minister, relatedFemaleForm, Madam Prime Minister]
-
A.
femaleHas
Indicates that a specified entity is female or possesses a female gender attribute in relation to another entity or context.
-
B.
hasFeminineFormInSomeLanguages
chosen
Indicates that the referenced entity has a distinct feminine grammatical or lexical form in at least one language.
-
C.
genderNeutralForm
Indicates that one entity is a gender-neutral linguistic form or expression corresponding to another, more gendered form.
-
D.
femaleMass
Indicates that the subject has a mass value specifically associated with its female form or female population.
-
E.
femaleAddressedAs
Indicates that a female individual is referred to or addressed by a particular name, title, or form of address.
- 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_69c6996180088190832e38e8d83ff54a |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c705257ca08190a78c592a1e616da8 |
completed | March 27, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69c7016df2b08190b2330a2010691431 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:08 p.m.