Triple
T9157273
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Martin Luther King Jr. Day |
E219736
|
entity |
| Predicate | banksOften |
P86830
|
FINISHED |
| Object | closed |
—
|
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: closed | Statement: [Martin Luther King Jr. Day, banksOften, closed]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: banksOften Context triple: [Martin Luther King Jr. Day, banksOften, closed]
-
A.
connectsBank
Indicates a relationship where one entity serves to link or provide access between another entity and a bank or banking service.
-
B.
hasBank
Indicates that one entity possesses, is associated with, or is served by a particular bank (such as a financial institution or river bank).
-
C.
hasFinancialInstitution
Indicates that one entity is associated with or linked to a financial institution, such as a bank or similar financial service provider.
-
D.
banking
Indicates that an entity provides or engages in financial services such as holding deposits, managing accounts, or facilitating monetary transactions for another entity.
-
E.
numberOfBanks
Indicates the quantity or count of banks associated with a given entity or context.
- 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_69ca83e25418819093c6503deeaf30de |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69cca9d6b9ac819094efe12c1ed67ecf |
completed | April 1, 2026, 5:15 a.m. |
| PD | Predicate disambiguation | batch_69cc6605c6808190a30d92da006206ac |
completed | April 1, 2026, 12:25 a.m. |
| PDg | Predicate description generation | batch_69cc668ad3d881908a8a93a6a1d553e4 |
completed | April 1, 2026, 12:27 a.m. |
Created at: March 30, 2026, 7:21 p.m.