Triple
T2491408
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Monroe, Michigan |
E52049
|
entity |
| Predicate | hasNickname |
P39
|
FINISHED |
| Object |
Monroe
Monroe is a city in southeastern Michigan known for its location along the River Raisin and its historical significance in the War of 1812.
|
E272403
|
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: Monroe | Statement: [Monroe, Michigan, hasNickname, Monroe]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Monroe Context triple: [Monroe, Michigan, hasNickname, Monroe]
-
A.
Monroe
Monroe is a Chicago 'L' rapid transit station located in the Loop and served by the Chicago Transit Authority's Red Line.
-
B.
Monroe
Monroe is a surname most famously associated with Earl Monroe, a Hall of Fame American basketball player known for his flashy playing style.
-
C.
Monroe
Monroe is a small city in North Carolina that serves as part of the greater Charlotte metropolitan area.
-
D.
Monroe
Monroe is a mid-sized city in northeastern Louisiana known as a regional hub for commerce, education, and culture along the Ouachita River.
-
E.
Monroe, Connecticut
Monroe, Connecticut is a suburban New England town in southwestern Connecticut known for its residential character, good schools, and location within commuting distance of New York City.
- 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: Monroe Triple: [Monroe, Michigan, hasNickname, Monroe]
Generated description
Monroe is a city in southeastern Michigan known for its location along the River Raisin and its historical significance in the War of 1812.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Monroe Target entity description: Monroe is a city in southeastern Michigan known for its location along the River Raisin and its historical significance in the War of 1812.
-
A.
Monroe
Monroe is a Chicago 'L' rapid transit station located in the Loop and served by the Chicago Transit Authority's Red Line.
-
B.
Monroe
Monroe is a surname most famously associated with Earl Monroe, a Hall of Fame American basketball player known for his flashy playing style.
-
C.
Monroe
Monroe is a small city in North Carolina that serves as part of the greater Charlotte metropolitan area.
-
D.
Monroe
Monroe is a mid-sized city in northeastern Louisiana known as a regional hub for commerce, education, and culture along the Ouachita River.
-
E.
Monroe, Connecticut
Monroe, Connecticut is a suburban New England town in southwestern Connecticut known for its residential character, good schools, and location within commuting distance of New York City.
- 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_69ab4955111c8190835bf619adec21ff |
completed | March 6, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69abd1914ca48190ab0a6cc5f1bd2f56 |
completed | March 7, 2026, 7:19 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69af1f9111ec8190b464da14bc4be11e |
completed | March 9, 2026, 7:29 p.m. |
| NEDg | Description generation | batch_69af209cb5dc8190b0a9870462205cd2 |
completed | March 9, 2026, 7:33 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69af2119cf10819083e0c66ae63eed2c |
completed | March 9, 2026, 7:35 p.m. |
Created at: March 6, 2026, 9:45 p.m.