Triple
T1940412
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Maine State Route 27 |
E41538
|
entity |
| Predicate | abbreviation |
P43
|
FINISHED |
| Object |
ME 27
ME 27 is a state highway in Maine that runs north–south, connecting coastal and central regions with the Canadian border.
|
E215982
|
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: ME 27 | Statement: [Maine State Route 27, abbreviation, ME 27]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ME 27 Context triple: [Maine State Route 27, abbreviation, ME 27]
-
A.
MEC
MEC is the commonly used acronym for Uruguay’s Ministry of Education and Culture, the national body responsible for educational policy and cultural affairs.
-
B.
MF 77
MF 77 is a steel-wheeled electric multiple unit train used on several lines of the Paris Métro, introduced in the late 1970s to modernize the network’s rolling stock.
-
C.
ME 100
ME 100 is a state highway in Maine that serves as a major north–south route connecting several communities and providing an alternative to Interstate 95.
-
D.
E-27
E-27 is the station code assigned to one of the platforms or lines serving Tokyo’s major transit hub, Shinjuku Station.
-
E.
M-72
M-72 is a state highway in northern Michigan that serves as a key east–west route connecting Traverse City with several inland communities and recreational areas.
- 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: ME 27 Triple: [Maine State Route 27, abbreviation, ME 27]
Generated description
ME 27 is a state highway in Maine that runs north–south, connecting coastal and central regions with the Canadian border.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ME 27 Target entity description: ME 27 is a state highway in Maine that runs north–south, connecting coastal and central regions with the Canadian border.
-
A.
MEC
MEC is the commonly used acronym for Uruguay’s Ministry of Education and Culture, the national body responsible for educational policy and cultural affairs.
-
B.
MF 77
MF 77 is a steel-wheeled electric multiple unit train used on several lines of the Paris Métro, introduced in the late 1970s to modernize the network’s rolling stock.
-
C.
ME 100
ME 100 is a state highway in Maine that serves as a major north–south route connecting several communities and providing an alternative to Interstate 95.
-
D.
E-27
E-27 is the station code assigned to one of the platforms or lines serving Tokyo’s major transit hub, Shinjuku Station.
-
E.
M-72
M-72 is a state highway in northern Michigan that serves as a key east–west route connecting Traverse City with several inland communities and recreational areas.
- 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_69a88649b24c819080047f26b6db2ded |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69abb2ca385c8190ad0e4bafbb9b5e5c |
completed | March 7, 2026, 5:08 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adf3fa84e48190bac4d96aa3c3ec39 |
completed | March 8, 2026, 10:11 p.m. |
| NEDg | Description generation | batch_69adf44290748190b882559de536af09 |
completed | March 8, 2026, 10:12 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69adf4d0ac58819096659706ef0785d0 |
completed | March 8, 2026, 10:14 p.m. |
Created at: March 4, 2026, 7:36 p.m.