Triple
T15643701
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ministry of Labor (Taiwan) |
E376126
|
entity |
| Predicate | shortName |
P43
|
FINISHED |
| Object |
MOL
MOL is the abbreviated name of Taiwan’s Ministry of Labor, the central government agency responsible for labor policy, employment affairs, and workers’ rights.
|
E1168257
|
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: MOL | Statement: [Ministry of Labor (Taiwan), shortName, MOL]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MOL Context triple: [Ministry of Labor (Taiwan), shortName, MOL]
-
A.
MOL
MOL is the vehicle registration code used on license plates for the Märkisch-Oderland district in the German state of Brandenburg.
-
B.
MOL
MOL is the IATA airport code for Molde Airport, Årø, which serves the town of Molde in Norway.
-
C.
MOL Global
MOL Global was a Malaysian online payment solutions provider best known for acquiring the once-popular social networking site Friendster.
-
D.
Uniper
Uniper is a German energy company focused on power generation and global energy trading, formed from the conventional energy business spun off from E.ON.
-
E.
Neste Oil
Neste Oil was the former name of Neste, a Finnish energy company best known today for its production of renewable fuels and sustainable energy solutions.
- 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: MOL Triple: [Ministry of Labor (Taiwan), shortName, MOL]
Generated description
MOL is the abbreviated name of Taiwan’s Ministry of Labor, the central government agency responsible for labor policy, employment affairs, and workers’ rights.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MOL Target entity description: MOL is the abbreviated name of Taiwan’s Ministry of Labor, the central government agency responsible for labor policy, employment affairs, and workers’ rights.
-
A.
MOL
MOL is the vehicle registration code used on license plates for the Märkisch-Oderland district in the German state of Brandenburg.
-
B.
MOL
MOL is the IATA airport code for Molde Airport, Årø, which serves the town of Molde in Norway.
-
C.
MOL Global
MOL Global was a Malaysian online payment solutions provider best known for acquiring the once-popular social networking site Friendster.
-
D.
Uniper
Uniper is a German energy company focused on power generation and global energy trading, formed from the conventional energy business spun off from E.ON.
-
E.
Neste Oil
Neste Oil was the former name of Neste, a Finnish energy company best known today for its production of renewable fuels and sustainable energy solutions.
- 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_69d85cd035a48190b73d5579ab73969a |
completed | April 10, 2026, 2:13 a.m. |
| NER | Named-entity recognition | batch_69e04ed400ec8190a14a9f7cf3092865 |
completed | April 16, 2026, 2:52 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ff5f4d49188190855a895c5844dee0 |
completed | May 9, 2026, 4:22 p.m. |
| NEDg | Description generation | batch_69ff614906cc81909d978d8645045af3 |
completed | May 9, 2026, 4:31 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ff61b3b3f08190a2a1e1010684a316 |
completed | May 9, 2026, 4:32 p.m. |
Created at: April 10, 2026, 4:15 a.m.