Triple
T4073318
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | "What can Brown do for you?" |
E86699
|
entity |
| Predicate | usedBy |
P260
|
FINISHED |
| Object | United Parcel Service |
E14654
|
NE 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: United Parcel Service | Statement: ["What can Brown do for you?", usedBy, United Parcel Service]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: United Parcel Service Context triple: ["What can Brown do for you?", usedBy, United Parcel Service]
-
A.
United Parcel Service (UPS)
chosen
United Parcel Service (UPS) is a global package delivery and supply chain management company known for its extensive logistics network and brown delivery trucks.
-
B.
FedEx
FedEx is a global courier delivery services company known for its overnight shipping and pioneering real-time package tracking.
-
C.
TNT Express
TNT Express is an international courier and logistics company known for its global parcel delivery and express mail services.
-
D.
YRC Worldwide
YRC Worldwide is a large American less-than-truckload (LTL) freight transportation and logistics company headquartered in Overland Park, Kansas.
-
E.
DHL
DHL is the Dag Hammarskjöld Library, the United Nations’ main research and information resource center located at its headquarters in New York.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
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_69aed93ebe448190a1f1686e28740ac9 |
completed | March 9, 2026, 2:29 p.m. |
| NER | Named-entity recognition | batch_69aefc22be988190a2b6575d4f5e0f7b |
completed | March 9, 2026, 4:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b562bc05948190a9ad709768420588 |
completed | March 14, 2026, 1:29 p.m. |
Created at: March 9, 2026, 3:39 p.m.