Triple
T32003635
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | FDNY Ladder Company 86 |
E817199
|
entity |
| Predicate | primaryApparatusType |
P137580
|
FINISHED |
| Object | aerial ladder truck |
—
|
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: aerial ladder truck | Statement: [FDNY Ladder Company 86, primaryApparatusType, aerial ladder truck]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: primaryApparatusType Context triple: [FDNY Ladder Company 86, primaryApparatusType, aerial ladder truck]
-
A.
hasTypeOfApparatus
chosen
Indicates that one entity is associated with, or utilizes, a specific kind or category of apparatus.
-
B.
describesApparatus
Indicates that one entity provides a description or specification of an apparatus used by another entity or within a particular context.
-
C.
hasCriticalApparatus
Indicates that a text or document is accompanied by a critical apparatus, i.e., scholarly notes, variant readings, and editorial commentary that analyze and support the main content.
-
D.
vehicleType
Indicates the specific kind or category of vehicle associated with an entity (e.g., car, bus, bicycle).
-
E.
mainVehicle
Indicates that one vehicle is the primary or most important vehicle associated with a given entity or context.
- 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_69f348f8ce388190ae84376b1f348f12 |
completed | April 30, 2026, 12:20 p.m. |
| NER | Named-entity recognition | batch_6a037c894b488190bcbec2eccaff4a01 |
completed | May 12, 2026, 7:16 p.m. |
| PD | Predicate disambiguation | batch_6a0379eaa540819095a1c5d9f3513f9b |
completed | May 12, 2026, 7:05 p.m. |
Created at: May 1, 2026, 12:14 a.m.