Triple
T16632896
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Syunik Province |
E404118
|
entity |
| Predicate | hasBorderTown |
P847
|
FINISHED |
| Object | Meghri |
E1224894
|
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: Meghri | Statement: [Syunik Province, hasBorderTown, Meghri]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Meghri Context triple: [Syunik Province, hasBorderTown, Meghri]
-
A.
Meghri
chosen
Meghri is a small town in southern Armenia known for its mild climate, historic churches, and location near the border with Iran.
-
B.
Koghb
Koghb is a village in northeastern Armenia known for its historic churches, archaeological sites, and scenic location near the border with Georgia.
-
C.
Metehara
Metehara is a town in central Ethiopia known for its sugar plantations and proximity to both the Awash National Park and Lake Basaka.
-
D.
Hamra
Hamra is a vibrant, cosmopolitan neighborhood in Beirut, Lebanon, known for its bustling commercial streets, cafes, and cultural life.
-
E.
Shemshak
Shemshak is a mountain village and ski resort in the Alborz range of northern Iran, known for its steep slopes and popularity among advanced skiers.
- 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_69d883897eb481909eaaa088ba9918d9 |
completed | April 10, 2026, 4:58 a.m. |
| NER | Named-entity recognition | batch_69e378e7d4a48190a9b4a14ecbb2a14b |
completed | April 18, 2026, 12:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a008a2b3a608190ad8d2b653cd8785d |
completed | May 10, 2026, 1:37 p.m. |
Created at: April 10, 2026, 5:17 a.m.