Triple
T2884
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Greater Boston |
E54
|
entity |
| Predicate | containsCity |
P294
|
FINISHED |
| Object | Everett |
E13
|
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: Everett | Statement: [Greater Boston, containsCity, Everett]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Everett Context triple: [Greater Boston, containsCity, Everett]
-
A.
Douglas
Douglas is a masculine given name of Scottish origin that has been widely used in English-speaking countries.
-
B.
Everett, Massachusetts, United States
chosen
Everett, Massachusetts, United States, is a small industrial city just north of Boston known historically for its manufacturing base and as the birthplace of influential engineer and science administrator Vannevar Bush.
-
C.
Delano
Delano is the middle name of Franklin D. Roosevelt, the 32nd president of the United States.
-
D.
Everett Public Schools
Everett Public Schools is the public school district serving students in the city of Everett, Massachusetts.
-
E.
San Francisco
San Francisco is a major coastal city in Northern California known for its hilly landscape, iconic Golden Gate Bridge, and role as a historic center of technology and counterculture.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (3 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_69a2328f0e848190ac2840eaf2d5ebd2 |
completed | Feb. 28, 2026, 12:10 a.m. |
| NER | Named-entity recognition | batch_69a234ccbac88190921bd0a15e4a7dbe |
completed | Feb. 28, 2026, 12:20 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69a243c57fe481909b6c1b8f41757f96 |
completed | Feb. 28, 2026, 1:24 a.m. |
Created at: Feb. 28, 2026, 12:13 a.m.