Triple
T612922
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Article I of the 1948 Genocide Convention |
E12138
|
entity |
| Predicate | legalCharacter |
P17112
|
FINISHED |
| Object | erga omnes partes obligation |
—
|
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: erga omnes partes obligation | Statement: [Article I of the 1948 Genocide Convention, legalCharacter, erga omnes partes obligation]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalCharacter Context triple: [Article I of the 1948 Genocide Convention, legalCharacter, erga omnes partes obligation]
-
A.
legalCharacterization
Indicates how an action, event, or situation is classified or characterized under a specific legal framework or set of laws.
-
B.
hasSpecialCharacter
Indicates that a given entity (such as a string or identifier) contains at least one non-alphanumeric special character.
-
C.
character2
Indicates that a second character entity is involved in the relationship or context defined by the predicate.
-
D.
containsCharacter
Indicates that one entity includes a specific character as part of its content or composition.
-
E.
characterSetName
Indicates the name assigned to a particular character set used for encoding or representing characters.
- 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_69a493309df48190a327f748e88049a6 |
completed | March 1, 2026, 7:27 p.m. |
| NER | Named-entity recognition | batch_69a49e08dbf88190ab050078a63e266b |
completed | March 1, 2026, 8:14 p.m. |
| PD | Predicate disambiguation | batch_69a49cfbcbf88190a854921dc531eba8 |
completed | March 1, 2026, 8:09 p.m. |
| PDg | Predicate description generation | batch_69a49def31ec81909dc53e70f4a36eda |
completed | March 1, 2026, 8:13 p.m. |
Created at: March 1, 2026, 7:35 p.m.