Triple
T37248664
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | JR Ticket Office |
E923931
|
entity |
| Predicate | hasCounterType |
P205796
|
FINISHED |
| Object | manned 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: manned counter | Statement: [JR Ticket Office, hasCounterType, manned counter]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasCounterType Context triple: [JR Ticket Office, hasCounterType, manned counter]
-
A.
hasCounterService
Indicates that a place provides service to customers over a counter, such as ordering, paying, or receiving items at a service counter.
-
B.
hasCounterSubject
Indicates that a subject is associated with another subject that serves as its counterpart, opposite, or contrasting entity in a given context.
-
C.
hasCountingDirection
Indicates the direction or order in which counting or enumeration proceeds between related entities.
-
D.
requiresCounter
Indicates that one entity must be opposed, balanced, or mitigated by another entity acting as a counter or counterpart.
-
E.
hasCountingPeriod
Indicates that there is a defined time span or interval over which occurrences, quantities, or measurements related to an entity are counted or aggregated.
- 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_69f76eaabb4c819093b751b139dad551 |
completed | May 3, 2026, 3:50 p.m. |
| NER | Named-entity recognition | batch_6a037cad051c8190b28b354b89208574 |
completed | May 12, 2026, 7:17 p.m. |
| PD | Predicate disambiguation | batch_6a037a11efc08190bb7cacc1325b4dc6 |
completed | May 12, 2026, 7:05 p.m. |
| PDg | Predicate description generation | batch_6a037c842b2c819082f1d2db995ac2eb |
completed | May 12, 2026, 7:16 p.m. |
Created at: May 3, 2026, 4:15 p.m.