Triple
T18611681
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Cybertron |
E454907
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Iacon |
—
|
NE NERFINISHED |
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: Iacon | Statement: [Cybertron, hasCity, Iacon]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Iacon Context triple: [Cybertron, hasCity, Iacon]
-
A.
Iacon
chosen
Iacon is a major city on the planet Cybertron in the Transformers universe, often depicted as the Autobots’ capital and primary stronghold.
-
B.
Ascra
Ascra was an ancient village in Boeotia, Greece, best known as the hometown of the poet Hesiod.
-
C.
Tosali
Tosali was an ancient city that served as a major political and administrative center of the Kalinga kingdom in eastern India.
-
D.
Aegium
Aegium was an important ancient Greek city in the region of Achaea, known as a political and religious center, especially during the Hellenistic period.
-
E.
Ambracia
Ambracia was an ancient Greek city in Epirus that became an important regional center and later the capital of King Pyrrhus.
- F. None of above.
- G. Unsure - the case is ambiguous/there is not enough information to decide.
Provenance (2 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_69d8d38bbe7c8190bdec3138e7d413c9 |
completed | April 10, 2026, 10:40 a.m. |
| NER | Named-entity recognition | batch_69e54d01ebd08190b6e646e3a749d7f5 |
completed | April 19, 2026, 9:45 p.m. |
Created at: April 10, 2026, 11:45 a.m.