Triple
T29480855
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Spartiates |
E747781
|
entity |
| Predicate | socialExclusionConsequence |
P36372
|
FINISHED |
| Object | loss of citizenship |
—
|
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: loss of citizenship | Statement: [Spartiates, socialExclusionConsequence, loss of citizenship]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: socialExclusionConsequence Context triple: [Spartiates, socialExclusionConsequence, loss of citizenship]
-
A.
violationConsequences
chosen
Indicates the negative outcomes, penalties, or repercussions that result from a violation of a rule, law, or agreement.
-
B.
socialImpact
Indicates the extent to which an action, entity, or relationship affects society or communities, whether positively or negatively.
-
C.
consequenceOfRevocation
Indicates that something occurs as a direct result of a prior revocation event or decision.
-
D.
censorshipConsequence
Indicates the outcome or impact that results from an act or policy of censorship being applied.
-
E.
causesSocialComplicationsFor
Indicates that one entity’s behavior, condition, or presence leads to difficulties, conflicts, or awkward situations in another entity’s social interactions or relationships.
- 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_69f0bd43ba30819095eb1cfc3adf525c |
completed | April 28, 2026, 1:59 p.m. |
| NER | Named-entity recognition | batch_69f6d6a6b04c8190bee4cf9c00665ef7 |
completed | May 3, 2026, 5:01 a.m. |
| PD | Predicate disambiguation | batch_69f6d26ceb08819091c71c001e954936 |
completed | May 3, 2026, 4:43 a.m. |
Created at: April 28, 2026, 4:03 p.m.