Triple
T15722715
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Sagay |
E381139
|
entity |
| Predicate | hasBarangay |
P29835
|
FINISHED |
| Object |
Taba-ao
Taba-ao is a barangay (village-level administrative division) of the municipality of Sagay in the Philippines.
|
E1173270
|
NE FINISHED |
How this triple was built (4 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: Taba-ao | Statement: [Sagay, hasBarangay, Taba-ao]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Taba-ao Context triple: [Sagay, hasBarangay, Taba-ao]
-
A.
Aralle-Tabulahan
Aralle-Tabulahan is an Austronesian language spoken by indigenous communities in the mountainous regions of West Sulawesi, Indonesia.
-
B.
Tagabawa
Tagabawa is an Austronesian language spoken by the Bagobo-Tagabawa people of Mindanao in the southern Philippines.
-
C.
Taba
Taba is a small Egyptian resort town on the Red Sea near the border with Israel, known for its beaches, coral reefs, and role as a popular gateway between the two countries.
-
D.
Tukabai
Tukabai was a wife of the Maratha nobleman Shahaji Bhonsle and a member of the early 17th-century Maratha aristocracy.
-
E.
Tutuyan
Tutuyan is a town in North Sulawesi, Indonesia, serving as the administrative and political center of East Bolaang Mongondow Regency.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Taba-ao Triple: [Sagay, hasBarangay, Taba-ao]
Generated description
Taba-ao is a barangay (village-level administrative division) of the municipality of Sagay in the Philippines.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Taba-ao Target entity description: Taba-ao is a barangay (village-level administrative division) of the municipality of Sagay in the Philippines.
-
A.
Aralle-Tabulahan
Aralle-Tabulahan is an Austronesian language spoken by indigenous communities in the mountainous regions of West Sulawesi, Indonesia.
-
B.
Tagabawa
Tagabawa is an Austronesian language spoken by the Bagobo-Tagabawa people of Mindanao in the southern Philippines.
-
C.
Taba
Taba is a small Egyptian resort town on the Red Sea near the border with Israel, known for its beaches, coral reefs, and role as a popular gateway between the two countries.
-
D.
Tukabai
Tukabai was a wife of the Maratha nobleman Shahaji Bhonsle and a member of the early 17th-century Maratha aristocracy.
-
E.
Tutuyan
Tutuyan is a town in North Sulawesi, Indonesia, serving as the administrative and political center of East Bolaang Mongondow Regency.
- F. None of above. chosen
Provenance (5 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_69d86d9bf930819082b30cf6d169297c |
completed | April 10, 2026, 3:25 a.m. |
| NER | Named-entity recognition | batch_69e04fb1fdd4819088f3e243263e5f73 |
completed | April 16, 2026, 2:55 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff82f68bf881909e5ad8a6ab81684a |
completed | May 9, 2026, 6:54 p.m. |
| NEDg | Description generation | batch_69ff8388b3588190ae55c123bb19cb2c |
completed | May 9, 2026, 6:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff84125e808190a4d465d9effad639 |
completed | May 9, 2026, 6:59 p.m. |
Created at: April 10, 2026, 4:45 a.m.