Triple
T14853310
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Genesis 29 |
E349285
|
entity |
| Predicate | setInLocation |
P40
|
FINISHED |
| Object | Haran |
E52250
|
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: Haran | Statement: [Genesis 29, setInLocation, Haran]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Haran Context triple: [Genesis 29, setInLocation, Haran]
-
A.
Haran
chosen
Haran is an ancient city in northern Mesopotamia known from the Hebrew Bible as a key dwelling place of the patriarch Abraham before his journey to Canaan.
-
B.
Haran
Haran is a biblical figure mentioned in the Book of Genesis, known as a member of Abraham’s extended family in the patriarchal narratives.
-
C.
Hapur
Hapur is a city in the Indian state of Uttar Pradesh, known as an industrial and grain market hub within the Delhi metropolitan area.
-
D.
Kfarhata
Kfarhata is a village located in the Koura District of northern Lebanon, known for its agricultural character and traditional rural setting.
-
E.
Kfarhay
Kfarhay is a small village located in the Batroun District of northern Lebanon, known for its rural character and traditional Lebanese landscape.
- 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_69d822ed7e1881909b90fca143ad7e34 |
completed | April 9, 2026, 10:06 p.m. |
| NER | Named-entity recognition | batch_69ded44318f0819080b6c599f2d3474f |
completed | April 14, 2026, 11:56 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fe6506ace48190819504b93f575660 |
completed | May 8, 2026, 10:34 p.m. |
Created at: April 10, 2026, 1:54 a.m.