Triple
T1338942
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Dublin Airport |
E28419
|
entity |
| Predicate | cityCenterDistance |
P1299
|
FINISHED |
| Object | approximately 10 km north of Dublin city centre |
—
|
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: approximately 10 km north of Dublin city centre | Statement: [Dublin Airport, cityCenterDistance, approximately 10 km north of Dublin city centre]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: cityCenterDistance Context triple: [Dublin Airport, cityCenterDistance, approximately 10 km north of Dublin city centre]
-
A.
distanceFromDowntown
chosen
Indicates the physical distance between a given location and the central downtown area.
-
B.
districtHeadquartersDistance
Indicates the distance between a place and its corresponding district headquarters.
-
C.
distanceCharacteristic
Indicates a relationship where an entity is described or constrained by some property or measure of distance (e.g., range, spacing, or separation).
-
D.
distanceFromCampusCenter
Indicates the measured or specified distance between a given location and the central point of the campus.
-
E.
distanceFromParisCenter
Indicates the measured distance between a given location and the central point of Paris.
- 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_69a49854eb3481908c7d56b2e449a290 |
completed | March 1, 2026, 7:49 p.m. |
| NER | Named-entity recognition | batch_69a4c21303c881908fef0b32831222fe |
completed | March 1, 2026, 10:47 p.m. |
| PD | Predicate disambiguation | batch_69a4bef174708190a07bbc697fe19a2d |
completed | March 1, 2026, 10:34 p.m. |
Created at: March 1, 2026, 7:56 p.m.