Triple
T30058749
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Haverhill, New Hampshire |
E763808
|
entity |
| Predicate | hasLandmark |
P105
|
FINISHED |
| Object |
Grafton County Courthouse
Grafton County Courthouse is a historic judicial building in Haverhill, New Hampshire, notable for its early American architecture and role in the county’s legal and civic history.
|
E1896876
|
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: Grafton County Courthouse | Statement: [Haverhill, New Hampshire, hasLandmark, Grafton County Courthouse]
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: Grafton County Courthouse Triple: [Haverhill, New Hampshire, hasLandmark, Grafton County Courthouse]
Generated description
Grafton County Courthouse is a historic judicial building in Haverhill, New Hampshire, notable for its early American architecture and role in the county’s legal and civic history.
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_69f224716378819087a722e487832b70 |
completed | April 29, 2026, 3:32 p.m. |
| NER | Named-entity recognition | batch_69f67ca0e7848190a9d4d7ca97f081a7 |
completed | May 2, 2026, 10:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a27324b1cf48190b6bf89f735e51280 |
completed | June 8, 2026, 9:21 p.m. |
| NEDg | Description generation | batch_6a2733ce52e88190965d0d7bb34b5282 |
completed | June 8, 2026, 9:27 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a27353bd1048190b1234556546bc2e7 |
completed | June 8, 2026, 9:33 p.m. |
Created at: April 29, 2026, 6:57 p.m.