Triple
T9212567
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Irvington |
E221159
|
entity |
| Predicate | adjacentTo |
P224
|
FINISHED |
| Object | Alameda |
E180395
|
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: Alameda | Statement: [Irvington, adjacentTo, Alameda]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Alameda Context triple: [Irvington, adjacentTo, Alameda]
-
A.
Alameda
Alameda is a major Lisbon metro and transport hub that serves as a key interchange point within the city's public transit network.
-
B.
Alameda
Alameda is the main central avenue of Santiago, Chile, serving as a key thoroughfare and symbolic axis of the city.
-
C.
Alameda, California
chosen
Alameda, California is a Bay Area island city adjacent to Oakland known for its historic Victorian architecture, waterfront parks, and residential neighborhoods.
-
D.
Santa Clara
Santa Clara is a Silicon Valley city in California known for its high-tech industry presence, Levi’s Stadium, and Santa Clara University.
-
E.
Santa Clara
Santa Clara is a major city in central Cuba known as the capital of Villa Clara Province and a historic site of key battles in the Cuban Revolution.
- 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_69ca83e9d0e081908bdb71097201a06c |
completed | March 30, 2026, 2:08 p.m. |
| NER | Named-entity recognition | batch_69ccda05406081909893bec3a092d3ce |
completed | April 1, 2026, 8:40 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d0660839f88190afdfb8bc2d710fc3 |
completed | April 4, 2026, 1:14 a.m. |
Created at: March 30, 2026, 7:27 p.m.