Triple
T24915201
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Munster Rugby |
E623963
|
entity |
| Predicate | primaryHomeCity |
P150859
|
FINISHED |
| Object | Limerick |
—
|
NE NERFINISHED |
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: Limerick | Statement: [Munster Rugby, primaryHomeCity, Limerick]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryHomeCity Context triple: [Munster Rugby, primaryHomeCity, Limerick]
-
A.
primaryLocationCity
Indicates the city that serves as the main or primary location associated with the subject.
-
B.
primaryCity
Indicates that one city serves as the main or most important city associated with a given region, entity, or context.
-
C.
homeCityOf
chosen
Indicates that a particular city is the primary place of residence or origin for a given person or organization.
-
D.
homeCityMetropolitanArea
Indicates that a specified city serves as the primary metropolitan area associated with a given entity’s home location.
-
E.
primaryTown
Indicates that a given town is the main or most important town associated with an entity (such as a person, organization, or region).
- 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_69e2fac889c081908e9ff686cb428e5a |
completed | April 18, 2026, 3:30 a.m. |
| NER | Named-entity recognition | batch_69f67f0488bc819089fbd2d2478158d3 |
completed | May 2, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69f67e3ed894819094c067c1ef624951 |
completed | May 2, 2026, 10:44 p.m. |
Created at: April 18, 2026, 5:28 a.m.