Triple
T1764462
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | New France |
E38729
|
entity |
| Predicate | notableCity |
P2813
|
FINISHED |
| Object | Mobile |
E32348
|
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: Mobile | Statement: [New France, notableCity, Mobile]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mobile Context triple: [New France, notableCity, Mobile]
-
A.
Mobile
chosen
Mobile is a historic port city on Alabama’s Gulf Coast known for its shipbuilding, cultural heritage, and hosting one of the oldest Mardi Gras celebrations in the United States.
-
B.
Office Mobile
Office Mobile is a mobile-optimized version of Microsoft Office that lets users view, edit, and create Office documents on smartphones and other portable devices.
-
C.
SIM
SIM is the commonly used abbreviation for the Science and Industry Museum in Manchester, a major UK museum dedicated to the history and impact of science, technology, and industry.
-
D.
iPhone
The iPhone is Apple's flagship smartphone line that revolutionized mobile technology by combining a touchscreen interface, internet connectivity, and a robust app ecosystem into a single device.
-
E.
Opera Mini
Opera Mini is a lightweight mobile web browser designed to compress data and load pages quickly, especially on slower networks and lower-end devices.
- 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_69a8862d562481908d7025a1c1f67c0d |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa646665088190afa31bdf48f14316 |
completed | March 6, 2026, 5:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ada0f12fd8819099759ebcdfc19494 |
completed | March 8, 2026, 4:16 p.m. |
Created at: March 4, 2026, 7:31 p.m.