Triple
T1732469
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Knesset |
E37843
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Givat Ram |
E163399
|
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: Givat Ram | Statement: [Knesset, locatedIn, Givat Ram]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Givat Ram Context triple: [Knesset, locatedIn, Givat Ram]
-
A.
Givat Ram
chosen
Givat Ram is a central neighborhood and campus area in Jerusalem that hosts major national institutions, including the Knesset and the Hebrew University’s main campus.
-
B.
Givatayim
Givatayim is a small, densely populated city in Israel’s Tel Aviv metropolitan area, known for its residential character and proximity to major urban centers.
-
C.
Rehovot
Rehovot is a city in central Israel known for its scientific and agricultural research institutions, including the Weizmann Institute of Science.
-
D.
Kiryat Ono
Kiryat Ono is a small suburban city in central Israel, located in the Tel Aviv metropolitan area.
-
E.
Herzliya
Herzliya is a coastal city in central Israel known as a high-tech and academic hub, home to major technology companies and institutions.
- 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_69a8861cc6ac8190ac0b2e31ccf62851 |
completed | March 4, 2026, 7:21 p.m. |
| NER | Named-entity recognition | batch_69aa639edaf88190b64b2f630a53f67c |
completed | March 6, 2026, 5:18 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69adeaca5f348190a8c1be9948d960e6 |
completed | March 8, 2026, 9:31 p.m. |
Created at: March 4, 2026, 7:30 p.m.