Triple
T26431303
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | State Route 99 Tunnel |
E664515
|
entity |
| Predicate | builtWith |
P11047
|
FINISHED |
| Object |
Bertha tunnel boring machine
Bertha was a massive tunnel boring machine used to excavate the State Route 99 tunnel beneath downtown Seattle, Washington.
|
E1724319
|
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: Bertha tunnel boring machine | Statement: [State Route 99 Tunnel, builtWith, Bertha tunnel boring machine]
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: Bertha tunnel boring machine Triple: [State Route 99 Tunnel, builtWith, Bertha tunnel boring machine]
Generated description
Bertha was a massive tunnel boring machine used to excavate the State Route 99 tunnel beneath downtown Seattle, Washington.
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_69ee883ad6a4819088f918e76122d690 |
completed | April 26, 2026, 9:48 p.m. |
| NER | Named-entity recognition | batch_69f611bdfed88190880458cf16b09a28 |
completed | May 2, 2026, 3:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a11aec878e0819084561b4a97d8a90c |
completed | May 23, 2026, 1:42 p.m. |
| NEDg | Description generation | batch_6a11af9c1be081909d2e461e3da596d6 |
completed | May 23, 2026, 1:46 p.m. |
| NED2 | Entity disambiguation (via description) | batch_6a11b051d328819090f947755dda4cfc |
completed | May 23, 2026, 1:49 p.m. |
Created at: April 26, 2026, 11:49 p.m.