Triple
T15693155
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GR-43 (Thessaly) |
E380384
|
entity |
| Predicate | hasComponent |
P35
|
FINISHED |
| Object | GR |
E970087
|
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: GR | Statement: [GR-43 (Thessaly), hasComponent, GR]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: GR Context triple: [GR-43 (Thessaly), hasComponent, GR]
-
A.
GR
GR is the stock ticker symbol for Goodrich Corporation, a former major American aerospace and defense company known for manufacturing aircraft systems and components.
-
B.
GR
GR is the standard abbreviation for Georgian Railway, the national rail transport company of Georgia.
-
C.
GR
GR is the performance and motorsport sub-brand of Toyota, known for developing high-performance road cars and competing in international racing series.
-
D.
GR
GR is the standard abbreviation for the extensive network of long-distance hiking trails known as the Grande Randonnée routes, primarily found across France and other European countries.
-
E.
GR
chosen
GR is the ISO 3166-1 alpha-2 country code for Greece, a southeastern European nation known for its ancient history and numerous islands.
- 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_69d86d99e860819094b6957cde470f2c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04f4f5a888190bd3681bcb9bbc02f |
completed | April 16, 2026, 2:54 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff6eed9a8c8190a57ffce61a27ec17 |
completed | May 9, 2026, 5:29 p.m. |
Created at: April 10, 2026, 4:44 a.m.