Triple
T11634355
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | GMA News and Public Affairs |
E276477
|
entity |
| Predicate | notableWork |
P4
|
FINISHED |
| Object | Saksi |
E274633
|
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: Saksi | Statement: [GMA News and Public Affairs, notableWork, Saksi]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Saksi Context triple: [GMA News and Public Affairs, notableWork, Saksi]
-
A.
Saksi
chosen
Saksi is a long-running Filipino late-night television newscast known for its in-depth reporting and broadcast on GMA Network.
-
B.
Sijilmasa
Sijilmasa was a medieval Moroccan oasis city that flourished as a key commercial hub linking North Africa with sub-Saharan gold and trade networks.
-
C.
Olay
Olay is a popular global skincare brand known for its anti-aging creams, moisturizers, and facial care products.
-
D.
Ein Siniya
Ein Siniya is a small Palestinian village in the central West Bank, known for its rural character and proximity to the town of Birzeit.
-
E.
Nasib
Nasib is a given name most notably borne by Nasib Yusifbeyli, an Azerbaijani statesman and political figure of the early 20th century.
- 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_69d6aafa51148190ab84940694c00235 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a25c0b00819095898d2b2445ecfb |
completed | April 10, 2026, 7:10 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ee87b12044819098a858edb2b16689 |
completed | April 26, 2026, 9:46 p.m. |
Created at: April 8, 2026, 9:39 p.m.