Triple
T18959487
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Afula railway station |
E463872
|
entity |
| Predicate | connectsTo |
P845
|
FINISHED |
| Object | Haifa |
—
|
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: Haifa | Statement: [Afula railway station, connectsTo, Haifa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haifa Context triple: [Afula railway station, connectsTo, Haifa]
-
A.
Haifa
chosen
Haifa is a major Israeli city on the Mediterranean coast, known for its significant port, mixed Jewish-Arab population, and the terraced Baháʼí Gardens on Mount Carmel.
-
B.
Ashdod
Ashdod is a major coastal city in southern Israel that serves as an important cultural and religious hub, including for the Karaite Jewish community.
-
C.
Ramla
Ramla is an Israeli city historically significant as a major religious and communal center for Karaite Jews.
-
D.
Tel Aviv
Tel Aviv is a major Israeli coastal city known for its vibrant nightlife, high-tech industry, and modernist architecture.
-
E.
Eilat
Eilat is Israel’s southernmost city and a major Red Sea resort and port known for its beaches, coral reefs, and tourism.
- 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_69d8dcffc278819086792a4ebfddfafa |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d5d1d784819095f3f886d7c47175 |
completed | April 20, 2026, 7:29 a.m. |
Created at: April 10, 2026, noon