Triple
T6671545
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Danaë |
E151741
|
entity |
| Predicate | associatedWithCity |
P1481
|
FINISHED |
| Object | Argos |
E70939
|
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: Argos | Statement: [Danaë, associatedWithCity, Argos]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Argos Context triple: [Danaë, associatedWithCity, Argos]
-
A.
Argos
Argos is the common nickname for the Toronto Argonauts, a professional Canadian Football League team based in Toronto.
-
B.
Argos
Argos is a major UK-based catalogue and online retailer known for offering a wide range of household goods, electronics, toys, and more through both physical stores and digital channels.
-
C.
Argos
chosen
Argos is one of the oldest continuously inhabited cities in Greece, located in the Peloponnese and historically significant as a major center of ancient Greek civilization.
-
D.
Argus
Argus is an early distributed programming language known for pioneering concepts in fault-tolerant, distributed systems and influencing modern object-oriented and concurrent programming.
-
E.
Argus
Argus is a many-eyed giant from Greek mythology best known for his role as a vigilant guardian.
- 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_69c687f71fc081909dbd45d6377f6045 |
completed | March 27, 2026, 1:36 p.m. |
| NER | Named-entity recognition | batch_69c6b0ca49f88190b9c8e0f641be0c3f |
completed | March 27, 2026, 4:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c6ef14b47c8190ac181f272025fb0d |
completed | March 27, 2026, 8:56 p.m. |
Created at: March 27, 2026, 2:03 p.m.