Triple
T20739709
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Queen’s Commendation for Valuable Service in the Air |
E509804
|
entity |
| Predicate | hasGenderNeutralEligibility |
P141325
|
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: [Queen’s Commendation for Valuable Service in the Air, hasGenderNeutralEligibility, true]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasGenderNeutralEligibility Context triple: [Queen’s Commendation for Valuable Service in the Air, hasGenderNeutralEligibility, true]
-
A.
hasGenderNeutrality
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.
-
B.
hasGenderRequirement
Indicates that a particular role, activity, or context specifies a required or restricted gender for participation or eligibility.
-
C.
includesBothGenders
Indicates that the referenced group, set, or category contains members of both male and female genders.
-
D.
hasNeutralPronoun
Indicates that an entity is referred to using a gender-neutral pronoun.
-
E.
genderNeutralSince
Indicates that an entity has been considered gender-neutral starting from a specific point in time.
- 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_69e0b4c589c08190834fb5d86d0efa2b |
completed | April 16, 2026, 10:07 a.m. |
| NER | Named-entity recognition | batch_69e6c20d9d4c8190a2fd87f8a33c313d |
completed | April 21, 2026, 12:17 a.m. |
| PD | Predicate disambiguation | batch_69e5c0509608819080cdbf47fcddfe36 |
completed | April 20, 2026, 5:57 a.m. |
| PDg | Predicate description generation | batch_69e5c3cbe5788190b7ace43bfdac2ef6 |
completed | April 20, 2026, 6:12 a.m. |
Created at: April 16, 2026, 12:32 p.m.