Triple
T16611224
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinckney State Recreation Area |
E403572
|
entity |
| Predicate | hasBodyOfWater |
P1778
|
FINISHED |
| Object |
Halfmoon Lake
Halfmoon Lake is a popular inland lake in Michigan known for swimming, boating, and fishing within the Pinckney State Recreation Area.
|
E2139755
|
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: Halfmoon Lake | Statement: [Pinckney State Recreation Area, hasBodyOfWater, Halfmoon Lake]
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: Halfmoon Lake Triple: [Pinckney State Recreation Area, hasBodyOfWater, Halfmoon Lake]
Generated description
Halfmoon Lake is a popular inland lake in Michigan known for swimming, boating, and fishing within the Pinckney State Recreation Area.
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_69d883880d0c81908b5fcd454e767b60 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e36096356c819092815d64db041793 |
completed | April 18, 2026, 10:44 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a38368e090c81908704ea549b322f2c |
completed | June 21, 2026, 7:07 p.m. |
| NEDg | Description generation | batch_6a3837e4a4008190a1a67886dd3dfc53 |
completed | June 21, 2026, 7:13 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a383848d8548190b6146c6d159ef00d |
completed | June 21, 2026, 7:15 p.m. |
Created at: April 10, 2026, 5:17 a.m.