Triple
T1335613
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Weeghman Park |
E28740
|
entity |
| Predicate | namedForOccupationOfNamesake |
P365
|
FINISHED |
| Object | restaurant owner |
—
|
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: restaurant owner | Statement: [Weeghman Park, namedForOccupationOfNamesake, restaurant owner]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: namedForOccupationOfNamesake Context triple: [Weeghman Park, namedForOccupationOfNamesake, restaurant owner]
-
A.
namesakeOccupation
chosen
Indicates that one entity’s occupation is the same as, or derived from, the occupation associated with the other entity’s namesake.
-
B.
namedPersonOccupation
Indicates that a person is explicitly identified as having a particular occupation or job role.
-
C.
honorificEponym
Indicates that one entity serves as an honorific namesake for another, typically recognizing or commemorating the person or entity in whose honor something is named.
-
D.
occupationOf
Indicates that one entity holds or performs the job, role, or profession associated with another entity.
-
E.
namedForNationality
Indicates that something is named after or in reference to a particular nationality or national identity.
- 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_69a498561a508190a3e1bc137c2b866a |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c1ecb5208190a9eadda113c91e66 |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef174708190a07bbc697fe19a2d |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:55 p.m.