Triple
T2187151
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Brasserie Lipp |
E49176
|
entity |
| Predicate | patronType |
P37185
|
FINISHED |
| Object | writers |
—
|
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: writers | Statement: [Brasserie Lipp, patronType, writers]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: patronType Context triple: [Brasserie Lipp, patronType, writers]
-
A.
patronState
Indicates a relationship where one state acts as a protector, sponsor, or dominant supporter of another state or political entity.
-
B.
patronage
Indicates a relationship where one party supports, sponsors, or protects another, often in exchange for loyalty, services, or influence.
-
C.
patronageFrom
Indicates a relationship where support, sponsorship, or backing is provided to someone or something by a patron.
-
D.
payerType
Indicates the type or category of entity responsible for making a payment in the relationship.
-
E.
probablePatron
Indicates that one entity is likely, but not certainly confirmed, to be the patron or sponsor of another entity.
- 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_69a88aa72d348190a9544bb5b8a4e71d |
completed | March 4, 2026, 7:40 p.m. |
| NER | Named-entity recognition | batch_69abbf9e99f08190892d34485c8f2f25 |
completed | March 7, 2026, 6:03 a.m. |
| PD | Predicate disambiguation | batch_69abbda32d1881909d1fd83a751fb21c |
completed | March 7, 2026, 5:54 a.m. |
| PDg | Predicate description generation | batch_69abbf9c77fc8190a323bcaf644fb2c5 |
completed | March 7, 2026, 6:03 a.m. |
Created at: March 4, 2026, 7:45 p.m.