Triple
T25741969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Military Cross |
E648242
|
entity |
| Predicate | eligibilityReformEffect |
P107365
|
FINISHED |
| Object | opened to all ranks |
—
|
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: opened to all ranks | Statement: [Military Cross, eligibilityReformEffect, opened to all ranks]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: eligibilityReformEffect Context triple: [Military Cross, eligibilityReformEffect, opened to all ranks]
-
A.
reformsBenefit
chosen
Indicates that certain reforms produce advantages, improvements, or positive outcomes for a particular entity or group.
-
B.
affectsBenefit
Indicates that one entity has an influence on, modifies, or determines the benefit or advantage received by another entity.
-
C.
legislatureAffected
Indicates that an action, event, or measure has an impact on a legislative body or its functioning.
-
D.
reapportionmentEffect
Indicates the impact or consequences that a reapportionment (such as redistricting or redistribution of representation) has on the related entities.
-
E.
repealEffect
Indicates that one legal act or decision nullifies, cancels, or removes the force or applicability of another.
- 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_69e7ab306eec8190b05c312c6ab186b8 |
completed | April 21, 2026, 4:52 p.m. |
| NER | Named-entity recognition | batch_69f6640168948190811bd5f933a87cf5 |
completed | May 2, 2026, 8:52 p.m. |
| PD | Predicate disambiguation | batch_69f6633451948190bcc0410602bb4914 |
completed | May 2, 2026, 8:48 p.m. |
Created at: April 22, 2026, 3:45 a.m.