Triple
T26025392
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kibbutz Ginnosar cemetery |
E647274
|
entity |
| Predicate | hasSectionFor |
P85325
|
FINISHED |
| Object | kibbutz veterans |
—
|
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: kibbutz veterans | Statement: [Kibbutz Ginnosar cemetery, hasSectionFor, kibbutz veterans]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSectionFor Context triple: [Kibbutz Ginnosar cemetery, hasSectionFor, kibbutz veterans]
-
A.
hasSectionWith
chosen
Indicates that an entity contains or includes a specific section that satisfies certain conditions or characteristics.
-
B.
hasSectionIn
Indicates that one entity contains or includes another entity as a section or subdivision within it.
-
C.
hasSectionOn
Indicates that one entity (typically a document or resource) contains a dedicated section or part that specifically addresses or discusses another entity or topic.
-
D.
hasSect
Indicates that an entity includes, contains, or is associated with a particular sect or subgroup within a larger religious, ideological, or organizational context.
-
E.
hasSectionAlong
Indicates that one entity includes or runs along a specific segment or portion of another 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_69e77e8b60e88190a3b26c4f0032a2c2 |
completed | April 21, 2026, 1:41 p.m. |
| NER | Named-entity recognition | batch_69f727afd5d88190ad48735cd1b32787 |
completed | May 3, 2026, 10:47 a.m. |
| PD | Predicate disambiguation | batch_69f72737c42c8190a3f781a5e98868ff |
completed | May 3, 2026, 10:45 a.m. |
Created at: April 22, 2026, 9:05 a.m.