Triple
T19776354
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Fort William Henry (Pemaquid) |
E475010
|
entity |
| Predicate | hasNearbySettlement |
P4647
|
FINISHED |
| Object |
New Harbor, Maine
New Harbor, Maine is a small coastal village in the town of Bristol known for its working harbor, scenic shoreline, and historic ties to the Pemaquid Peninsula.
|
E2286754
|
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: New Harbor, Maine | Statement: [Fort William Henry (Pemaquid), hasNearbySettlement, New Harbor, Maine]
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: New Harbor, Maine Triple: [Fort William Henry (Pemaquid), hasNearbySettlement, New Harbor, Maine]
Generated description
New Harbor, Maine is a small coastal village in the town of Bristol known for its working harbor, scenic shoreline, and historic ties to the Pemaquid Peninsula.
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_69d8e51a43a08190956bc6df13c91a77 |
completed | April 10, 2026, 11:55 a.m. |
| NER | Named-entity recognition | batch_69e65360cca08190957c36b1a440542c |
completed | April 20, 2026, 4:25 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a46f653c3bc81909e65e65a168b8609 |
completed | July 2, 2026, 11:37 p.m. |
| NEDg | Description generation | batch_6a46f73513e48190b1f678271587bf1d |
completed | July 2, 2026, 11:41 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a47207736b48190a48dffe7c4971574 |
completed | July 3, 2026, 2:37 a.m. |
Created at: April 10, 2026, 1:48 p.m.