Triple
T12048606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Lord Lieutenant of Suffolk |
E286851
|
entity |
| Predicate | isGenderNeutralTitle |
P37803
|
FINISHED |
| Object | true |
—
|
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: true | Statement: [Lord Lieutenant of Suffolk, isGenderNeutralTitle, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: isGenderNeutralTitle Context triple: [Lord Lieutenant of Suffolk, isGenderNeutralTitle, true]
-
A.
hasGenderedTitle
Indicates that an entity is associated with a title or form of address that is explicitly marked for a particular gender.
-
B.
usedBothMaleAndFemaleTitles
Indicates that an entity has been referred to or addressed using both male and female honorifics or titles.
-
C.
isIndividualTitle
Indicates that a given title refers to a single, specific individual rather than a group, organization, or collective entity.
-
D.
hasGenderNeutrality
chosen
Indicates that something (such as a term, form, or expression) is neutral with respect to gender and does not specify or imply any particular gender.
-
E.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
- 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_69d6ab4780948190bdb9f7620c2ac27e |
completed | April 8, 2026, 7:23 p.m. |
| NER | Named-entity recognition | batch_69d9100b4ca8819084845ca4c13e34ce |
completed | April 10, 2026, 2:58 p.m. |
| PD | Predicate disambiguation | batch_69d902bac9e08190aa1a99c835f29542 |
completed | April 10, 2026, 2:01 p.m. |
Created at: April 8, 2026, 9:47 p.m.