Triple
T4656139
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Daphne |
E102413
|
entity |
| Predicate | locatedNear |
P294
|
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: [Daphne, locatedNear, Mobile]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Mobile Context triple: [Daphne, locatedNear, 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.
TalkTalk Mobile
TalkTalk Mobile is a UK-based mobile virtual network operator offering mobile phone services as part of TalkTalk's wider telecommunications portfolio.
-
C.
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.
-
D.
Love Mobiles
Love Mobiles are elaborately decorated, music-blasting parade trucks that serve as moving dance floors and party platforms during Zurich’s Street Parade.
-
E.
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.
- 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_69bd43d823288190952279faa0d1d066 |
completed | March 20, 2026, 12:55 p.m. |
| NER | Named-entity recognition | batch_69bd63193a108190a7d9aec1d1d40cf8 |
completed | March 20, 2026, 3:09 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69bdfaf28c148190b46cf846528034c5 |
completed | March 21, 2026, 1:57 a.m. |
Created at: March 20, 2026, 1:14 p.m.