Triple
T10707819
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | San Juan, Trinidad and Tobago |
E252451
|
entity |
| Predicate | nearbySettlement |
P350
|
FINISHED |
| Object | Tunapuna |
E563382
|
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: Tunapuna | Statement: [San Juan, Trinidad and Tobago, nearbySettlement, Tunapuna]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Tunapuna Context triple: [San Juan, Trinidad and Tobago, nearbySettlement, Tunapuna]
-
A.
Tunapuna
chosen
Tunapuna is a major town and commercial center in the northern part of Trinidad, Trinidad and Tobago.
-
B.
Pakurumo
Pakurumo is a popular Afrobeat song by Nigerian artist Wizkid, known for its upbeat rhythm and dance-friendly vibe.
-
C.
Tunasan
Tunasan is a barangay and district in the southern part of Muntinlupa City in Metro Manila, Philippines.
-
D.
Lapa
Lapa is a historic and bohemian neighborhood in Rio de Janeiro, Brazil, famous for its vibrant nightlife, samba clubs, and iconic aqueduct arches.
-
E.
Tambolaka
Tambolaka is a town on the Indonesian island of Sumba that serves as an important local hub with an airport and access point for exploring the island.
- 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_69d6aa5cbabc8190973e683950d89faf |
completed | April 8, 2026, 7:19 p.m. |
| NER | Named-entity recognition | batch_69d6fde080d48190830eaa863aad61ff |
completed | April 9, 2026, 1:16 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69d9990760b48190a05753974cdf556c |
completed | April 11, 2026, 12:42 a.m. |
Created at: April 8, 2026, 9:13 p.m.