Triple
T236905
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | United Provinces |
E4842
|
entity |
| Predicate | alsoKnownAs |
P39
|
FINISHED |
| Object | UP |
E10261
|
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: UP | Statement: [United Provinces, alsoKnownAs, UP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: UP Context triple: [United Provinces, alsoKnownAs, UP]
-
A.
UP
chosen
UP is the standard reporting mark used to identify rail equipment owned or operated by the Union Pacific Railroad in North America.
-
B.
UPP
UPP is a reporting mark used by the Union Pacific Railroad to identify certain passenger cars and related rolling stock in its fleet.
-
C.
UPY
UPY is a reporting mark used by Union Pacific Railroad, primarily identifying its yard and switching locomotives.
-
D.
LU
LU is the two-letter ISO 3166-1 alpha-2 country code assigned to Luxembourg for international identification and data standards.
-
E.
HUP
HUP is a major academic medical center in Philadelphia that serves as the flagship teaching hospital of the University of Pennsylvania's health system.
- 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_69a257c3d0708190b0871c4269d273e6 |
completed | Feb. 28, 2026, 2:49 a.m. |
| NER | Named-entity recognition | batch_69a25ccc5d548190b505bf1d99db41bd |
completed | Feb. 28, 2026, 3:11 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a3673474548190aea1f43318d15a71 |
completed | Feb. 28, 2026, 10:07 p.m. |
Created at: Feb. 28, 2026, 2:53 a.m.