Triple
T23364565
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | DHL Newlands |
E593277
|
entity |
| Predicate | sponsor |
P67
|
FINISHED |
| Object | DHL |
—
|
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: DHL | Statement: [DHL Newlands, sponsor, DHL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DHL Context triple: [DHL Newlands, sponsor, DHL]
-
A.
DHL
DHL is the Dag Hammarskjöld Library, the United Nations’ main research and information resource center located at its headquarters in New York.
-
B.
DHL
chosen
DHL is a global logistics and courier company known for its international express mail, freight transportation, and supply chain management services.
-
C.
TNT Express
TNT Express is an international courier and logistics company known for its global parcel delivery and express mail services.
-
D.
FedEx
FedEx is a global courier delivery services company known for its overnight shipping and pioneering real-time package tracking.
-
E.
Blue Dart
Blue Dart was a named passenger train that operated on the New York, Chicago and St. Louis Railroad (Nickel Plate Road), providing intercity rail service in the United States.
- 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_69e25d2593c88190bcdf4a716a94ccb2 |
completed | April 17, 2026, 4:17 p.m. |
| NER | Named-entity recognition | batch_69f1a0ab7fc481908b496ec9b543eddd |
completed | April 29, 2026, 6:09 a.m. |
Created at: April 17, 2026, 5:31 p.m.