Triple
T15304602
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Hatta Club |
E365866
|
entity |
| Predicate | locatedIn |
P40
|
FINISHED |
| Object | Hatta |
E157605
|
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: Hatta | Statement: [Hatta Club, locatedIn, Hatta]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Hatta Context triple: [Hatta Club, locatedIn, Hatta]
-
A.
Hatta
chosen
Hatta is an Indonesian surname most prominently associated with Mohammad Hatta, the country’s first vice president and a leading figure in the struggle for independence.
-
B.
Haruru
Haruru is a small settlement in New Zealand’s Bay of Islands region, known for its scenic surroundings and proximity to Haruru Falls.
-
C.
Moru
Moru is a Central Sudanic language spoken primarily by the Moru people in South Sudan.
-
D.
Kahama
Kahama is a town and district-level administrative center in northwestern Tanzania known for its mining activities and role as a commercial hub in the Shinyanga area.
-
E.
Gohatto
Gohatto is a 1999 Japanese period drama film directed by Nagisa Ōshima that explores forbidden desire and tensions within the samurai ranks of the Shinsengumi.
- 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_69d85a113ee881908e297a1d38dd79fa |
completed | April 10, 2026, 2:01 a.m. |
| NER | Named-entity recognition | batch_69e03ccef14c819099c5ebe962e7f867 |
completed | April 16, 2026, 1:35 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69fef89d961481909be8dcc2864982c9 |
completed | May 9, 2026, 9:04 a.m. |
Created at: April 10, 2026, 3:15 a.m.