Triple
T13331662
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Abraham Moss Centre |
E317588
|
entity |
| Predicate | namedAfter |
P63
|
FINISHED |
| Object | Abraham Moss |
E317587
|
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: Abraham Moss | Statement: [Abraham Moss Centre, namedAfter, Abraham Moss]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Abraham Moss Context triple: [Abraham Moss Centre, namedAfter, Abraham Moss]
-
A.
Abraham Moss
chosen
Abraham Moss is an area in Manchester, England, known for its community facilities and as a stop on the Manchester Metrolink light rail system.
-
B.
Isaac Abuhav
Isaac Abuhav was a prominent 15th-century Spanish rabbi and kabbalist whose teachings and legacy are commemorated by the historic Abuhav Synagogue in Safed.
-
C.
Abraham Paskowitz
Abraham Paskowitz is a filmmaker and entrepreneur best known for creating the mobile video-sharing app Socialcam.
-
D.
Israel Abrahams
Israel Abrahams was a British Jewish scholar and author renowned for his influential works on Jewish history, literature, and religious life.
-
E.
Abraham Benrubi
Abraham Benrubi is an American character actor known for his roles in television series like "ER" and "Parker Lewis Can't Lose" as well as various film appearances.
- 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_69d806b4d62c81908d4ced1665414be5 |
completed | April 9, 2026, 8:06 p.m. |
| NER | Named-entity recognition | batch_69d9992fffa0819086610ae3bed2e2f9 |
completed | April 11, 2026, 12:43 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f71f36e4cc819093007404ceb5da31 |
completed | May 3, 2026, 10:11 a.m. |
Created at: April 9, 2026, 9:30 p.m.