Triple
T22253203
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Idhna |
E550029
|
entity |
| Predicate | hasNearbyLocality |
P3883
|
FINISHED |
| Object | Tarqumiyah |
—
|
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: Tarqumiyah | Statement: [Idhna, hasNearbyLocality, Tarqumiyah]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tarqumiyah Context triple: [Idhna, hasNearbyLocality, Tarqumiyah]
-
A.
Tarqumiyah
chosen
Tarqumiyah is a Palestinian town located in the Hebron Governorate in the southern West Bank.
-
B.
Talbiya
Talbiya is a historic, upscale neighborhood in central Jerusalem known for its elegant architecture, cultural institutions, and proximity to major city landmarks.
-
C.
Tabiriyya
Tabiriyya is a sub-school within the Zaydi branch of Shia Islam, representing a distinct theological and legal tradition in that sect.
-
D.
Hedaya
Hedaya is a surname most notably associated with American character actor Dan Hedaya, known for his numerous film and television roles.
-
E.
Lawāmiʿ
Lawāmiʿ is a later Persian Sufi treatise, traditionally attributed to Jāmī, that elaborates on mystical themes also treated in his earlier work Lawāʾiḥ.
- 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_69e11e42adb8819087714772ea606709 |
completed | April 16, 2026, 5:37 p.m. |
| NER | Named-entity recognition | batch_69f138c003548190889860d6163eb873 |
completed | April 28, 2026, 10:46 p.m. |
Created at: April 16, 2026, 8:39 p.m.