Triple
T23718655
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Inniskilling |
E586078
|
entity |
| Predicate | linkedTown |
P69581
|
FINISHED |
| Object | Enniskillen |
—
|
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: Enniskillen | Statement: [Inniskilling, linkedTown, Enniskillen]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: linkedTown Context triple: [Inniskilling, linkedTown, Enniskillen]
-
A.
linkedCity
Indicates that two entities are associated with each other through a specific city, such as being located in, connected via, or related by that city.
-
B.
linkedLocation
Indicates that one location is associated or connected to another location in a meaningful way, such as being related, referenced, or contextually tied.
-
C.
connectsCountyTown
Indicates a relationship where a county is linked or associated with a town, typically signifying that the town lies within or is administered by that county.
-
D.
connectsMunicipalities
Indicates a relationship where one entity serves as a link or route that joins two or more municipalities.
-
E.
partnerTown
chosen
Indicates that two towns are formally linked through a partnership or twinning relationship, often for cultural, economic, or administrative cooperation.
- 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_69e24906fb108190a6898751e46bdc11 |
completed | April 17, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69f1b77d121c8190892d3f8ef3f3156f |
completed | April 29, 2026, 7:47 a.m. |
| PD | Predicate disambiguation | batch_69f155e4b1148190836ede4741dcb888 |
completed | April 29, 2026, 12:50 a.m. |
Created at: April 17, 2026, 6:59 p.m.