Triple
T27763136
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stavros G. Livanos |
E701526
|
entity |
| Predicate | hasNotableInLaw |
P62104
|
FINISHED |
| Object | Aristotle Onassis |
—
|
NE NERFINISHED |
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: Aristotle Onassis | Statement: [Stavros G. Livanos, hasNotableInLaw, Aristotle Onassis]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasNotableInLaw Context triple: [Stavros G. Livanos, hasNotableInLaw, Aristotle Onassis]
-
A.
hasLegalRelevanceIn
Indicates that something is legally significant, applicable, or has consequences within a specified legal context, case, or jurisdiction.
-
B.
containsLaw
Indicates that one entity (such as a document, code, or jurisdiction) includes or encompasses a specific law within it.
-
C.
haveLaw
Indicates that a governing body or jurisdiction possesses, enforces, or is characterized by a particular law or set of laws.
-
D.
notableAreaOfLaw
chosen
Indicates that a person or entity is particularly recognized or distinguished in a specific field or area of law.
-
E.
notableLegalCode
Indicates that a legal code is especially significant, influential, or noteworthy in relation to the referenced entity.
- 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_69ef6a52fa708190934a32308d2c92dc |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69fec4cffed08190b5e5e7cc0c87493e |
completed | May 9, 2026, 5:23 a.m. |
| PD | Predicate disambiguation | batch_69fec2ea7fe08190bd751b39515f69d1 |
completed | May 9, 2026, 5:15 a.m. |
Created at: April 27, 2026, 4:28 p.m.