Triple
T2958930
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Panzer II |
E79998
|
entity |
| Predicate | manufacturer |
P490
|
FINISHED |
| Object |
FAMO
FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
|
E313395
|
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: FAMO | Statement: [Panzer II, manufacturer, FAMO]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: FAMO Context triple: [Panzer II, manufacturer, FAMO]
-
A.
FAMET
FAMET is the Spanish Army’s aviation branch responsible for operating and supporting its helicopter and air mobility units.
-
B.
F.A.C.
F.A.C. is the standard legal abbreviation used to refer to the Florida Administrative Code, which contains the administrative rules and regulations of the state of Florida.
-
C.
FALA
FALA is the ICAO airport code for Lanseria International Airport, a major privately owned international airport serving the Johannesburg region in South Africa.
-
D.
FAMAS F1
The FAMAS F1 is a French bullpup 5.56×45mm NATO assault rifle variant widely recognized for its distinctive design and long service with the French military.
-
E.
Fon
Fon is a major Gbe language of West Africa, primarily spoken by the Fon people in Benin and neighboring countries.
- 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: FAMO Triple: [Panzer II, manufacturer, FAMO]
Generated description
FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: FAMO Target entity description: FAMO was a German vehicle manufacturer best known for producing military half-tracks and armored vehicles for the Wehrmacht during World War II.
-
A.
FAMET
FAMET is the Spanish Army’s aviation branch responsible for operating and supporting its helicopter and air mobility units.
-
B.
F.A.C.
F.A.C. is the standard legal abbreviation used to refer to the Florida Administrative Code, which contains the administrative rules and regulations of the state of Florida.
-
C.
FALA
FALA is the ICAO airport code for Lanseria International Airport, a major privately owned international airport serving the Johannesburg region in South Africa.
-
D.
FAMAS F1
The FAMAS F1 is a French bullpup 5.56×45mm NATO assault rifle variant widely recognized for its distinctive design and long service with the French military.
-
E.
Fon
Fon is a major Gbe language of West Africa, primarily spoken by the Fon people in Benin and neighboring countries.
- 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_69ad8b1341848190bd19dbf46892887d |
completed | March 8, 2026, 2:43 p.m. |
| NER | Named-entity recognition | batch_69ad992c4c7c819084b5bef299255181 |
completed | March 8, 2026, 3:43 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69b0fc8df3f481908ba71ab72e68938e |
completed | March 11, 2026, 5:24 a.m. |
| NEDg | Description generation | batch_69b0fd174d3c8190b57a98ba324ce93c |
completed | March 11, 2026, 5:26 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69b0fda9e3e08190a765ebd814de8466 |
completed | March 11, 2026, 5:29 a.m. |
Created at: March 8, 2026, 2:57 p.m.