Triple
T4912463
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Uppland |
E110264
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object | Tierp |
E146819
|
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: Tierp | Statement: [Uppland, contains, Tierp]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tierp Context triple: [Uppland, contains, Tierp]
-
A.
Tierp
chosen
Tierp is a locality and municipal seat in east-central Sweden known for its rural surroundings and motorsport activities, including the Tierp Arena drag racing track.
-
B.
Tepiman
Tepiman is a subgroup of Uto-Aztecan languages spoken primarily in the southwestern United States and northern Mexico, including languages such as O'odham and Tepehuán.
-
C.
Tars
Tars is the athletic nickname for the sports teams representing Rollins College.
-
D.
Tukker
Tukker is a given name or surname that functions as a variant spelling of the name Tucker.
-
E.
Typer
Typer is a modern, user-friendly Python library for building command-line interfaces, created by Sebastián Ramírez (tiangolo), that emphasizes type hints and automatic documentation.
- 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_69bd44132b94819088522d92beaadc78 |
completed | March 20, 2026, 12:56 p.m. |
| NER | Named-entity recognition | batch_69bd6e9c32148190a940a3733ecd1898 |
completed | March 20, 2026, 3:58 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69be6fe7a0f48190b666202a97b32c7d |
completed | March 21, 2026, 10:16 a.m. |
Created at: March 20, 2026, 1:29 p.m.