Triple
T17877889
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Mid-Michigan |
E447004
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object |
Midland
Midland is a small industrial and cultural city in central Michigan known historically for its strong ties to the chemical industry and community-focused amenities.
|
E1293439
|
NE FINISHED |
How this triple was built (4 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: [Mid-Michigan, containsCity, Midland]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Midland Context triple: [Mid-Michigan, containsCity, Midland]
-
A.
Midland
Midland was a short-lived Formula One constructor that competed in the mid-2000s after taking over the Jordan Grand Prix team.
-
B.
Midland
Midland is a small town in central Ontario, Canada, known as a gateway to Georgian Bay and the 30,000 Islands region.
-
C.
Midland
Midland is a major commercial and transport hub in the eastern suburbs of Perth, Western Australia.
-
D.
Midland
Midland is a city in the Permian Basin region of West Texas known for its pivotal role in the oil and gas industry.
-
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. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Midland Triple: [Mid-Michigan, containsCity, Midland]
Generated description
Midland is a small industrial and cultural city in central Michigan known historically for its strong ties to the chemical industry and community-focused amenities.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Midland Target entity description: Midland is a small industrial and cultural city in central Michigan known historically for its strong ties to the chemical industry and community-focused amenities.
-
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
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. chosen
Provenance (5 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_69d8b9f4c22c819093c2680434472894 |
completed | April 10, 2026, 8:51 a.m. |
| NER | Named-entity recognition | batch_69e49c0c46108190b8edef2572b5ba90 |
completed | April 19, 2026, 9:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a0313d1c1e48190a17e3b81c4ee22f3 |
completed | May 12, 2026, 11:49 a.m. |
| NEDg | Description generation | batch_6a031485c3908190a8fe49068d45e910 |
completed | May 12, 2026, 11:52 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a03151de8408190a4c8bc30f5606951 |
completed | May 12, 2026, 11:55 a.m. |
Created at: April 10, 2026, 10:18 a.m.