Triple
T27609005
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mudka's Meat Hut |
E700261
|
entity |
| Predicate | hasCounter |
P88384
|
FINISHED |
| Object | order counter |
—
|
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: order counter | Statement: [Mudka's Meat Hut, hasCounter, order counter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounter Context triple: [Mudka's Meat Hut, hasCounter, order counter]
-
A.
hasCounterSubject
Indicates that a subject is associated with another subject that serves as its counterpart, opposite, or contrasting entity in a given context.
-
B.
hasCounterService
chosen
Indicates that a place provides service to customers over a counter, such as ordering, paying, or receiving items at a service counter.
-
C.
usesCounters
Indicates that one entity employs or relies on counters (such as tallying or tracking mechanisms) in relation to another entity or process.
-
D.
usesCounterLength
Indicates that one entity determines or measures something based on the length of a counter value.
-
E.
hasCheckInCounters
Indicates that an entity is associated with one or more check-in counters used for processing arrivals or registrations.
- 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_69ef6a4e2e208190b63b7268f405785c |
completed | April 27, 2026, 1:53 p.m. |
| NER | Named-entity recognition | batch_69ff21cbd9108190a52c0ba42004c669 |
completed | May 9, 2026, noon |
| PD | Predicate disambiguation | batch_69ff1faea91881908c626c70bca5100a |
completed | May 9, 2026, 11:51 a.m. |
Created at: April 27, 2026, 2:10 p.m.