Triple
T15850188
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Moab son of Lot |
E384313
|
entity |
| Predicate | hasSibling |
P363
|
FINISHED |
| Object | Ben-Ammi |
E657264
|
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: Ben-Ammi | Statement: [Moab son of Lot, hasSibling, Ben-Ammi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Ben-Ammi Context triple: [Moab son of Lot, hasSibling, Ben-Ammi]
-
A.
Ben-Ammi
chosen
Ben-Ammi is a biblical figure described in the Book of Genesis as the progenitor of the Ammonite people.
-
B.
Ben-Ami
Ben-Ami is a Hebrew surname borne by various Israeli public figures, including politicians, academics, and artists.
-
C.
Benzion
Benzion is a Hebrew masculine given name meaning "son of Zion," traditionally used in Jewish communities.
-
D.
Aharoni
Aharoni is a Hebrew surname derived from the given name Aharon (Aaron), commonly associated with Jewish families of Levantine origin.
-
E.
Berko Shemets
Berko Shemets is a half-Tlingit, half-Jewish police detective in Michael Chabon’s novel *The Yiddish Policemen’s Union*, known for his complex identity and partnership with protagonist Meyer Landsman.
- 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_69d86da422088190aac39e32e6c68429 |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e14cab0fe48190bd6629e071761e91 |
completed | April 16, 2026, 8:55 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ffa93fbcb481908e7b7ddc46992f79 |
completed | May 9, 2026, 9:38 p.m. |
Created at: April 10, 2026, 4:50 a.m.