Triple
T3365729
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Stuart Park |
E70830
|
entity |
| Predicate | hasNeighbouringSuburb |
P41355
|
FINISHED |
| Object | Parap |
E60401
|
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: Parap | Statement: [Stuart Park, hasNeighbouringSuburb, Parap]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Parap Context triple: [Stuart Park, hasNeighbouringSuburb, Parap]
-
A.
Parap
chosen
Parap is an inner-city suburb of Darwin in Australia's Northern Territory, known for its popular weekend markets and tropical, laid-back atmosphere.
-
B.
Parag
Parag is a given name most notably associated with Parag Agrawal, the former CEO of Twitter.
-
C.
Parl
Parl is the commonly used abbreviation for the Parliament of Singapore, the country's unicameral legislative body.
-
D.
The Paras
The Paras are an elite airborne infantry regiment of the British Army renowned for their rigorous training, rapid deployment capabilities, and distinguished combat history.
-
E.
Pan
Pan is a 1894 novel by Norwegian author Knut Hamsun, known for its lyrical portrayal of nature and its psychologically intense depiction of love and jealousy.
- 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_69ad85a729d48190afd789cd8417f289 |
completed | March 8, 2026, 2:20 p.m. |
| NER | Named-entity recognition | batch_69adb28643f48190b78b0222f8323344 |
completed | March 8, 2026, 5:31 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b3254daf8c8190b2141682503c111e |
completed | March 12, 2026, 8:42 p.m. |
Created at: March 8, 2026, 3:13 p.m.