Triple
T15843312
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Tiago Monteiro |
E384150
|
entity |
| Predicate | F1TeamsDrivenFor |
P52116
|
FINISHED |
| Object | Midland |
E384147
|
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: Midland | Statement: [Tiago Monteiro, F1TeamsDrivenFor, Midland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Midland Context triple: [Tiago Monteiro, F1TeamsDrivenFor, Midland]
-
A.
Midland
Midland is a city in the Permian Basin region of West Texas known for its pivotal role in the oil and gas industry.
-
B.
Midland
chosen
Midland was a short-lived Formula One constructor that competed in the mid-2000s after taking over the Jordan Grand Prix team.
-
C.
Midland
Midland is a small town in central Ontario, Canada, known as a gateway to Georgian Bay and the 30,000 Islands region.
-
D.
Midland
Midland is a major commercial and transport hub in the eastern suburbs of Perth, Western Australia.
-
E.
Midland City
Midland City is a fictional Midwestern American town created by Kurt Vonnegut that serves as the primary setting for several of his novels.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e142ea3da08190a9d2d5917f84907c |
completed | April 16, 2026, 8:13 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa1412c9481909808473e14058033 |
completed | May 9, 2026, 9:04 p.m. |
Created at: April 10, 2026, 4:50 a.m.