Triple
T19003901
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Basilan |
E465025
|
entity |
| Predicate | hasCity |
P316
|
FINISHED |
| Object | Lamitan |
—
|
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: Lamitan | Statement: [Basilan, hasCity, Lamitan]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lamitan Context triple: [Basilan, hasCity, Lamitan]
-
A.
Lamitan
chosen
Lamitan is a city on Basilan Island in the southern Philippines, known as a commercial and administrative center with a history marked by both cultural diversity and security challenges.
-
B.
Lahan
Lahan is a municipality and commercial hub in southeastern Nepal known for its role as a regional trade and transport center.
-
C.
Lambuyao
Lambuyao is a barangay (village-level administrative division) within the municipality of Oton in the province of Iloilo, Philippines.
-
D.
Lamanay
Lamanay is an alternative spelling of Lamanai, a major ancient Maya archaeological site in northern Belize known for its impressive temples and long period of continuous occupation.
-
E.
Kalamansig
Kalamansig is a coastal municipality in the province of Sultan Kudarat in the Philippines, known for its fishing industry and diverse indigenous communities.
- 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_69d8dd01a56c81909694a128c66b21d7 |
completed | April 10, 2026, 11:20 a.m. |
| NER | Named-entity recognition | batch_69e5d6a3db9c8190a1bf0228e360a6e0 |
completed | April 20, 2026, 7:32 a.m. |
Created at: April 10, 2026, 12:01 p.m.