Triple
T442401
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | St. Patrick's Day |
E10139
|
entity |
| Predicate | legalStatusInMontserrat |
P13227
|
FINISHED |
| Object | public holiday |
—
|
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: public holiday | Statement: [St. Patrick's Day, legalStatusInMontserrat, public holiday]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: legalStatusInMontserrat Context triple: [St. Patrick's Day, legalStatusInMontserrat, public holiday]
-
A.
hasLegalStatus
Indicates that an entity possesses a particular legal classification, recognition, or standing under law.
-
B.
legalStatusInManyCountries
Indicates that the subject has a particular legal classification or standing that is recognized across numerous countries.
-
C.
legalStatusOfCuba
Indicates the legal or diplomatic status that an entity or action has specifically in relation to Cuba.
-
D.
statusInMaryland
Indicates that an entity holds a particular legal, regulatory, or operational status specifically within the jurisdiction of Maryland.
-
E.
legalStatusOfElectors
Indicates the legal standing, rights, and recognition granted to electors under applicable laws or regulations.
- 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_69a2e8465ef481909655c681b01e2986 |
completed | Feb. 28, 2026, 1:06 p.m. |
| NER | Named-entity recognition | batch_69a2ef42b4008190abed9d79926c7022 |
completed | Feb. 28, 2026, 1:36 p.m. |
| PD | Predicate disambiguation | batch_69a2edde2b9c8190bd20b582eb4c5065 |
completed | Feb. 28, 2026, 1:30 p.m. |
| PDg | Predicate description generation | batch_69a2eeb9e6b0819093863959a6e5730a |
completed | Feb. 28, 2026, 1:33 p.m. |
Created at: Feb. 28, 2026, 1:11 p.m.