Triple
T21259127
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Warendorf district |
E523949
|
entity |
| Predicate | hasVehicleRegistrationCode |
P1173
|
FINISHED |
| Object | WAF |
—
|
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: WAF | Statement: [Warendorf district, hasVehicleRegistrationCode, WAF]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: WAF Context triple: [Warendorf district, hasVehicleRegistrationCode, WAF]
-
A.
WAF
chosen
WAF is the vehicle registration code used on license plates for vehicles registered in the district of Warendorf in North Rhine-Westphalia, Germany.
-
B.
WAF
WAF is the three-letter station code used to identify Watford tube station on the London Underground network.
-
C.
AWS WAF
AWS WAF is a cloud-based web application firewall service that helps protect web applications and APIs from common web exploits and bots.
-
D.
Alibaba Cloud WAF
Alibaba Cloud WAF is a cloud-based web application firewall service that protects websites and applications on Alibaba Cloud from common web attacks, bots, and malicious traffic.
-
E.
AIOWF
AIOWF is the collective body representing the international federations that govern sports featured in the Olympic Winter Games.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69e0b5156d7881909bd4f83676590715 |
completed | April 16, 2026, 10:08 a.m. |
| NER | Named-entity recognition | batch_69e735e53df88190bd6024793a0ada08 |
completed | April 21, 2026, 8:31 a.m. |
Created at: April 16, 2026, 3:59 p.m.