Triple
T14241220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | University of New Hampshire School of Law |
E353010
|
entity |
| Predicate | city |
P40
|
FINISHED |
| Object |
Concord
Concord is the capital city of New Hampshire in the United States, known as a regional center for government, law, and education.
|
E405416
|
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: Concord | Statement: [University of New Hampshire School of Law, city, Concord]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Concord Context triple: [University of New Hampshire School of Law, city, Concord]
-
A.
Concord
Concord is a suburban city in California’s East Bay region known as a residential and commercial hub within the greater San Francisco Bay Area.
-
B.
Concord
Concord is a suburban neighbourhood within the city of Vaughan in Ontario, Canada, known for its mix of industrial, commercial, and residential areas.
-
C.
Concord
Concord is a historic New England town in eastern Massachusetts known for its pivotal role in the American Revolutionary War and its association with prominent 19th-century literary figures.
-
D.
Concord
Concord is a widely grown American grape variety known for its deep purple color and use in sweet wines, juices, and jellies.
-
E.
Concord
Concord is a residential and commercial district within the town of Washington in Tyne and Wear, England.
- 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: Concord Triple: [University of New Hampshire School of Law, city, Concord]
Generated description
Concord is the capital city of New Hampshire in the United States, known as a regional center for government, law, and education.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Concord Target entity description: Concord is the capital city of New Hampshire in the United States, known as a regional center for government, law, and education.
-
A.
Concord
chosen
Concord is the capital city of the U.S. state of New Hampshire, known for its historic role in American politics and its location along the Merrimack River.
-
B.
Concord
Concord is a historic New England town in eastern Massachusetts known for its pivotal role in the American Revolutionary War and its association with prominent 19th-century literary figures.
-
C.
Concord
Concord is a growing suburban city in North Carolina that forms part of the greater Charlotte metropolitan area.
-
D.
Concord
Concord is a suburban city in California’s East Bay region known as a residential and commercial hub within the greater San Francisco Bay Area.
-
E.
Concord
Concord is a residential and commercial district within the town of Washington in Tyne and Wear, England.
- F. None of above.
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_69d8278adc7c8190a9218d69bce3c4e6 |
completed | April 9, 2026, 10:26 p.m. |
| NER | Named-entity recognition | batch_69de6244ad188190b9d9db7914240410 |
completed | April 14, 2026, 3:50 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fd3d1081148190b8830615a34711c0 |
completed | May 8, 2026, 1:32 a.m. |
| NEDg | Description generation | batch_69fd3f09579081908b229d0ad9befea3 |
completed | May 8, 2026, 1:40 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69fd3f5763e48190a4482cc01774adf4 |
completed | May 8, 2026, 1:41 a.m. |
Created at: April 10, 2026, 1:08 a.m.