Triple
T19853750
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Londonderry Fire Department |
E477075
|
entity |
| Predicate | hasTypeOfApparatus |
P137580
|
FINISHED |
| Object | fire engines |
—
|
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: fire engines | Statement: [Londonderry Fire Department, hasTypeOfApparatus, fire engines]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasTypeOfApparatus Context triple: [Londonderry Fire Department, hasTypeOfApparatus, fire engines]
-
A.
describesApparatus
Indicates that one entity provides a description or specification of an apparatus used by another entity or within a particular context.
-
B.
hasCabType
Indicates that an entity is associated with or characterized by a specific type or category of cab.
-
C.
hasCraneType
Indicates that an entity is associated with or classified by a specific type of crane.
-
D.
hasAuxiliaryEquipment
Indicates that one entity is equipped with, or accompanied by, additional supporting equipment associated with another entity.
-
E.
hasSignatureEquipment
Indicates that an entity is associated with a distinctive or characteristic piece of equipment that is uniquely or notably linked to it.
- 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_69d8e51d39d081909bcfafeaaf3d2fcc |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e6586aa1dc8190b6cfe051a57e338b |
completed | April 20, 2026, 4:46 p.m. |
| PD | Predicate disambiguation | batch_69e537e21d2881909b1be82f02b99d40 |
completed | April 19, 2026, 8:15 p.m. |
| PDg | Predicate description generation | batch_69e543c136b081909cab9394b958390a |
completed | April 19, 2026, 9:06 p.m. |
Created at: April 10, 2026, 1:51 p.m.