Triple
T5599877
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Anif Cemetery |
E147090
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Anif |
E22894
|
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: Anif | Statement: [Anif Cemetery, locatedIn, Anif]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Anif Context triple: [Anif Cemetery, locatedIn, Anif]
-
A.
Anif
chosen
Anif is a small Austrian municipality near Salzburg, known for its historic castle and as a residence of notable figures.
-
B.
Anini
Anini is a remote town in the Dibang Valley district of Arunachal Pradesh in northeastern India, known for its rugged Himalayan terrain and proximity to the Dibang River.
-
C.
Anafi
Anafi is a small, remote Greek island in the Aegean Sea, known for its tranquil beaches, rugged landscapes, and traditional Cycladic architecture.
-
D.
Anahorish
Anahorish is a poem by Seamus Heaney that evokes the landscape and memories of his rural Northern Irish childhood through rich, place-based imagery.
-
E.
Afif
Afif is a town in central Saudi Arabia known as an inland community within the Riyadh administrative region.
- 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_69c009043d648190a7af89698ccf1e3e |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c020d936dc8190a2e599f1df9fdd91 |
completed | March 22, 2026, 5:03 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0287139508190aa646918228cfdc0 |
completed | March 22, 2026, 5:35 p.m. |
Created at: March 22, 2026, 3:38 p.m.