Triple
T35355260
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Calhoun County, Texas |
E1021304
|
entity |
| Predicate | contains |
P35
|
FINISHED |
| Object |
Magnolia Beach, Texas
Magnolia Beach, Texas is a small coastal community and popular bayside recreation area on the shores of Lavaca Bay in Calhoun County.
|
E2136779
|
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: Magnolia Beach, Texas | Statement: [Calhoun County, Texas, contains, Magnolia Beach, Texas]
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: Magnolia Beach, Texas Triple: [Calhoun County, Texas, contains, Magnolia Beach, Texas]
Generated description
Magnolia Beach, Texas is a small coastal community and popular bayside recreation area on the shores of Lavaca Bay in Calhoun County.
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_69f76def44c881908a20e8008572eb44 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69f79198ba208190a618fb7ea384cb63 |
completed | May 3, 2026, 6:19 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a3823d79eac8190a964bf51ecac722f |
completed | June 21, 2026, 5:48 p.m. |
| NEDg | Description generation | batch_6a38254665c88190b7dd9d0767aec8e9 |
completed | June 21, 2026, 5:54 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a38260b8ef08190b7ddb0b1bbd8c4d2 |
completed | June 21, 2026, 5:57 p.m. |
Created at: May 3, 2026, 4:03 p.m.