Triple
T38254373
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panther Hollow Bridge |
E1017743
|
entity |
| Predicate | hasNameOrigin |
P3325
|
FINISHED |
| Object |
Panther Hollow valley
Panther Hollow valley is a wooded ravine in Pittsburgh’s Schenley Park, known for its stream, lake, and surrounding parkland.
|
E2263076
|
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: Panther Hollow valley | Statement: [Panther Hollow Bridge, hasNameOrigin, Panther Hollow valley]
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: Panther Hollow valley Triple: [Panther Hollow Bridge, hasNameOrigin, Panther Hollow valley]
Generated description
Panther Hollow valley is a wooded ravine in Pittsburgh’s Schenley Park, known for its stream, lake, and surrounding parkland.
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_69f76de33e4481909099fa812709bd42 |
completed | May 3, 2026, 3:46 p.m. |
| NER | Named-entity recognition | batch_69fcb1a277688190a265d0b16d6fa236 |
completed | May 7, 2026, 3:37 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a4193cec290819090715b23763f0465 |
completed | June 28, 2026, 9:36 p.m. |
| NEDg | Description generation | batch_6a41947a2f608190aba9f20c7e4cd8d6 |
completed | June 28, 2026, 9:39 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a4195210170819086828d7780a6e407 |
completed | June 28, 2026, 9:41 p.m. |
Created at: May 3, 2026, 4:30 p.m.